Robert Mueller’s Deflective Force Field

Robert Mueller and His Deflective Forcefield

On July 24th at 8:32 AM EST, all eyes and ears were turned to the former special counsel, the honorable Robert Mueller. Going into the hearings, the Republicans hoped to expose multiple structural cracks in the report. The Democrats, on the other hand, tried to get just one conclusive evidence of collusion and election tampering to justify impeachment.

Just like other interested citizens, I have been following the Russian collusion and DNC email hacking saga since 2016, so naturally, I expected that Mueller would address some of the key findings in the report, but alas, my hopes for insight and clarity were dashed. What promised to be simple Q&A session turned out to be a painful, 454 minute game of charades where you never get to figure out any of the answers.

At 8:54 AM, 48 minutes and 18 seconds into his sworn testimony, Robert Mueller–the consummate DC political bureaucrat–activated his industrial strength fog machine and deployed a force field deflector shield. This set in motion a reoccurring pattern of ducking, dodging, and sidestepping direct and specific questions about his pet project report.

Despite the lack of clarity in his answers and his alarming unfamiliarity with his own work (e.g. not knowing who Fusion GPS was), I found the session to be insightful and a veritable treasure trove in terms of body language, image artifacts, and audio content worthy of analysis. Mueller spoke for about 7 hours and provided a rare opportunity to capture his conversational patterns, facial characteristics, and behavioral fingerprint when under duress while in a single continual homogeneous session–and all of this in a well lit environment in front of high resolution cameras. For video analytics, It don’t get no better than this!

A Note About Lie Detection
Nonverbal queues or AKA body language is a form of communication. It is similar to verbal communications expect that it’s done through facial expressions, gestures, touching, physical movements, posture, bling, tone, timbre, and various speech and voice characteristics. Nonverbal behavior comprises a large percent of all interpersonal communication and can provided insight into a person’s thoughts and feelings.

The theory behind the ability to detect lies from body language is that most people who are lying find it difficult to maintain physical and mental comfort under ongoing questioning. The result is observable distress in their speech and appearance. This is because disguising the truth requires significant amount of left brain creative processing, that in turn, increases cognitive load as the person struggles to ‘make up’ answers to what would otherwise be fast memory recollection responses.

That said, there is no such thing as an accurate lie detector. Polygraphs or professional body language readers can only spot person’s discomfort and stress as they relate to certain topics of conversation and then focus on these areas for further analysis. If the annals of polygraph testing teach us anything, it is that professional liars like Aldrich Ames, Robert Hanssen, and Kim Philby (who ironically wrote the chapter about catching double agents), were resistant to lie detection.

It is also relevant to note that criminal courts usually don’t accept polygraph tests or body language reading as evidence because they are considered unreliable by academic psychologists (Christine Blasey Ford may disagree with this finding) and by reputable scientists. In addition, the person who administers and assesses the test has a great deal of control over how the test is conducted and its outcome. This, by itself, can completely skew or invalidate the test.

An Experienced Counter Intelligence Officer
When evaluating Mueller’s testimony, it is important to remember that he is a professional with years of experience in debriefings (over 80 congressional testimonies), legal depositions, interrogations, and counter intelligence work. This was evident in his testimony. With a few exceptions, he avoided taking the bait from hostile questioners’ and utilized common counter-interrogation techniques such as draining the clock by asking for questions to be repeated (18 times), requesting the speaker to cite and point to the specific references in his copy of the documents (9 times), endlessly paging though his folder without finding or reading any of the referenced content (7 times), and answering at length about unrelated issues.

Mueller’s most frequent deflection tactic was to to use I-phrases such as “I can’t get into…” or “I’m not going to…”. The former special special counsel declined to answer all relevant questions about topics such as the Steele Dossier, Fusion GPS, the usage of paid informants, and the genealogy of the FISA applications. As can be seen in Table 1, out of about 230 total questions, Mueller dodged about 198 and only provided vague non-committal responses to 10 others. This amounted to failing to answer about 87% of all questions. 

This was quite a performance for the shining knight of justice, especially if you consider the DOJ mission statement of:

“To enforce the law and defend the interests of the United States according to the law; to ensure public safety against threats foreign and domestic; to provide federal leadership in preventing and controlling crime; to seek just punishment for those guilty of unlawful behavior; and to ensure fair and impartial administration of justice for all Americans.

The key operative word here is “ensure”, not try, attempt, or do your best, but to verify and confirm.

Mueller Deflection Timeline
Chart 1: The distribution of Mueller’s instances of dodging or refusing to answer questions during his testimony

Mueller’s Response Algorithm
Mueller was selective in what questions he deflected. To the casual observer, it may have seemed that he was laconic across the board, but that wasn’t’ the case. In multiple non–sequential instances, he provided elaborate and definitive responses to questions but these were almost exclusively from Democratic Congress and Intelligence Oversight Committee members. With a few exceptions, most of his verbose responses could be categorized as being damaging to President Trump.

Mueller Keyword Cloud
Image 1
: Mueller’s Tag Cloud of the types of words and phrases that he used to avoid answering the questions. The operative sentence that proceeded most of these words was ”I’m not going to…

As can be seen in Table 1, the taxonomy of his answers contains a large variation of the first person “I”, “I’m”, and “my”. This suggests that Mueller felt a strong affinity to the document. He never used the form “we”, “our”, or “the team” which would have been more appropriate considering his repeated assertions that the report was a large team effort and that no single individual has mastered its content.



Response to Question



I stick with the language that is in front of you



I will leave the answer to our report



I’m not going to discuss other matters



I’ll refer to the report






I can’t say I understand the statistics



I direct you to the report for how its characterized



I rely on the language in the report



This is one of those area which I decline to discuss and will direct you to the report



Again, I send you to the report



I have to pass on that



I rely on the report



This is outside my purview



That is outside my purview



Outside my purview



I refer you to the report



This is still outside my purview



I will refer you to the report on that episode



I’m going to ask you rely on what we wrote about that incident



I’m again would refer you to the report and the way its characterized in the report



I’m not going to get into that



I can’t get into that. That’s internal deliberation of the justice department



I direct you again to the report



Whatever was said will be in the report



I can’t answer that questions



That’s not in my purview



I can’t get into that



I can’t get into that



I am not going to get into it



I would refer you to the coverage of this in the report



I would refer you to the report



I send you back to the report



I refer you to the write-up of this in the report



I can’t beyond what’s in the report



I can’t get into internal deliberations



I can’t get into the evidentiary findings



Can’t get into that



I will leave it as it appears in the report



I’m just going to have to refer you to the report if I could



I don’t want to speculate



I rely on the wording of the report



With regards to Steele, that beyond my purview



It’s not within my purview



As I said before and said again, it’s not within my purview



I refer you to the report on that



That’s an area in which I cannot get into



I’m not going to get into what we may or may not have included in our investigation



I’m not going to get into subsidiary details. I refer you again to the page 91-92



I can’t speak to that



I am under orders that don’t allow me to give you an answer to that particular question



I can’t get into the discussion on that



I’m not going to be involved in the discussion on that…



I’m not going to go further in terms of discussion…



I can’t get into our investigative moves



I’m not going to get into that any further than I already have



I can’t speak to that



I would say I rely on what’s in the report



That letter speaks for itself



I’m not going to go beyond that



I refer you to the court proceedings on that issue



I’m not going to get into that



I can’t speak to that



I’m not going to talk to that



I’m not going to speak that



I’m not going to get into what was in Mr. Comey’s mind



I’m not going to delve more into the details of what happened



I’ll leave that to the attorney General



I’m not going to get into ta discussion on that



Again, I refer you to the report



I refer you to the lengthy dissertation on exactly whose issues that appears in the report



I can’t speak to that



That was outside out purview



I’m not going to speak to that



And I am not going to answer that question, sir



I’m not going to speak anymore to that



I’m not going to answer that



I have nothing to add



I’m not going to add to what I have stated before



I feel uncomfortable discussing anything to do with the Stone indictment



I’m not going to speculate



I’m not going top discuss that



Not going to talk about that



I’m not going to answer that



I’m not going to talk about that issue



I’m not going to get into that. It’s a little of track



I have to say the letter itself speaks for itself



I go back to the latter. The letter speaks for itself



I can’t answer that question in a vacuum



We have not specified the persons mentioned



I’m not going to speculate



I’m going to pass on that



I’m not going to comment



I’m not going to go into details of the report



Those areas, I’m going to stay away from



I’m not going to get into those matters to which you refer



I’m not going to speak to the series of happenings as you explained them



I’d have to refer you to the reports on that one



I’m not going to speculate



I can speak to the half of the half of your question that’s on the screen being accurate



I’m not going to speak to that



Again, I’m not going to discuss the issues related to Mr. Steele



Again, I pass on answering that question



That’s about all I’ll say on this aspect of it



I’m going to pass on that



I take your question



I’m not going to speculate along those lines



I’m not going to opine on that. I don’t have the expertise in that arena to opine



I cannot agree with that. Not that it’s not true, but hat I can’t agree with it…



That portion or that matter does not fall within our jurisdiction



I direct you to the report for how its characterized



I’m not going to discuss any other alternatives



I can’t speak to that. That would be in levels of classification



I’m going to stay away from one particular or two particular situations



I’m not going to talk about specifics



I’m not going to speak to that



I’m not going to get into that. It goes into internal deliberations



Again, I’m going to pass on that



As I said before, this is an area that I cannot speak to



Again, I’m not going to speak to that issue



Questions such as that should go to the FBI



And I’m not going to discuss that



I’m not going to get into that



And again, I’m not going to respond to that



Again, I can’t respond



Again, I can’t speak to it



Again, I can’t answer that



Again, I’m not going to go there



I think you understand I cannot get into either classified or law enforcement information



I can’t respond to that question, it’s outside my jurisdiction



Again, I can’t speak to that



I can’t go into it



I’m no longer in the Federal government, so I’ll pass



I don’t want to wade into those waters



I defer to the report on that



I can’t get into a discussion on it



I can’t answer that



I can’t get into that



Again, it’s the same territory that I’m loath to get into



I’m not going to talk to that



I’m not going to talk to that



That I can’t get into



And I can’t get into that area



I can’t answer that question



I’m not going to get into that



I cannot get into that



I will not get into that



I leave that to you



Again, speculation

Table 1: Sampling of reasons from about 200 instances for Mueller’s refusal to answer questions

The Evaluation Process
Mueller’s testimony consisted of over 750,000 video frames. Evan a trained interrogator could only process a small percentage of this data. Add to this the observer’s distraction, blinking, and fatigue and it becomes virtually impossible for a human to be able to accurately capture the fine nuances of all of these these frames or sequences for content. At best, a person would be able to provide a summarized ‘gut feeling’ about the overall session and reference some vague (and often inaccurate) actions such as ‘he touched his nose’ which could suggest that he was lying.

AI based video analytics on the other hand, can easily process each video frame in a consistent, repeatable manner, and with no observer bios. The objective of my evaluation of Mueller’s testimony was not to determine if he was lying with certainty, but rather to identify recurring patterns of stress that are associated with deception and correlate them to the topics of conversation.

Mueller did a great job obfuscating the report details but the large high quality volume of video and audio in his testimony made it possible to analyze the session and find anomalies and various patterns that could provide insight into his mindset

In this project just as in several of my previous posts (1, 2, 3), I used AI based video analytics, text, and speech analysis platforms. These included:

For the text/speech, I used a hybrid approach to word and phrase speech pattern analysis. The textual analysis evaluated these types of speech categories:

  • I-words (I, Me, My, I’m)
  • Social words
  • Positive emotions
  • Negative emotions
  • Cognitive processes
  • Analytic reasoning
  • Clout
  • Authenticity
  • Emotional tone

For the video analytics, I established Mueller’s facial and other video objects baseline using several on-line sources and the main testimony video. The baseline cataloging included his unique facial expressions such as Microexpressions and other visually detectable actions like use of hand gesture, hand related activities, head motion, mouth movement, gaze, etc.

Image 2: Sampling of Mueller’s Microexpressions such as (L-R): loathing/anger, surprise, fear, happiness

Sampling of Mueller’s Body Mechanics
Image 3: Sampling of Mueller’s’ body dynamics as related to left hand usage

Following the creation of a facial baseline catalog, I proceeded with the ML training using his unique data sets for non-facial activity such as paging through the report folder, eye blink rate, gaze, etc.

Sample of Training Sets for Paging Detection
Image 4: Sampling of image set used to train the machine learning (ML) to identify Mueller flipping pages through his report folders 

After the training was completed, I ran the first 15 minutes of Mueller’s testimony through the engine and performed a search for known classified objects such as him ‘reading the report’.

Mueller Reading the Report
Imager 5
: Sample search results of instances of Mueller looking at the report

I noted the detections and examined several thousand video frames prior, during, and after the detections to capture the actual ground truth. The visual search results of the 15 minute video segment correlated to within a 83% match rate against the baseline catalog created with the ML training set. I then used the missed detections to re-train the ML again and repeated this cycle several times on random video segments of his testimony until the match rate stabilized at about 94%.

In addition to creating a catalog of Mueller’s microexpressions I also created a library of sequences of his composite facial expressions. These sequences were close consecutively spaced combinations of microexpressions and other body activity that were 0.5-3.5 seconds long. One example for these types of composite expressions was eye flutter combined with ‘lip twitching’ or some other mouth movement.

In this sequence, Mueller typically stared at the speaker while his bottom lip would involuntary twitch or quiver several times or his lips would tighten; he would then break eye contact with the speaker and rotate his head downwards, recompose, then bring his head upwards and re-establish eye contact with the speaker.  

Image 6: Sample of a typical Mueller sequence showing mouth activity and breaking eye contact with the speaker. The context here is Rep Jim Jordan’s asking Mueller to confirm if Joseph Mifsud was interviewed, did he lie, and is he Russian or Western Intelligence

Once I completed calibrating Mueller’s video object catalog and the library of sequential expressions, I conducted searches for facial anomalies. Anomalies are defined as any variations from his standard single image or sequence patterns such as unusual cycle of head, eye, or mouth movements. 

For example, based on his standard detection for “blinking”, Mueller’s blinking interval baseline was established to be 3–7 seconds with a blinking duration of approximately 1/10th-1/3rd of a second (see Image 7-8). 

Mueller’s Natural Blink Cycle
Image 7: Sample of one baseline feature in Mueller’s visual object catalog showing his normal blink pattern.

Sample of Training Sets for Blinking Detection
Image 8: Sample detections of Mueller normal blinking pattern throughout his testimony. Mueller blinking follows a pattern of a full single closure of the eyelid at a 3-7 second interval

Any blinking variation form this base line generated an anomaly that was then evaluated manually before becoming certified as a new pattern of interest. This exception was then further evaluated in the context of the topic of conversation and the microexpressions involved. 

One such anomaly was associated with Mueller’s unusual blinking pattern. On closer examination, it turned out that what on the surface appeared to be unusual blinking was in fact a reoccurring cycle of rapid flutter of the eyelids. This unusual sequence was also at times accompanied by certain head, tongue, and lip movements.

After mapping this ‘Flutter Cycle” to the topic that was being discussed at the time of the event, it became clear that this was some sort of an involuntary display of distress and/or fear. It was so prevalent that it could even be used to predict what questions were being discussed.

Some of the subjects that triggered this ‘Flutter Cycle’ were: 

  • DOJ and FBI media leaks
  • Christopher Steele, the dossier and its funding sources
  • Fusion GPS and its work with the DNC, HRC, and foreign governments
  • Glen Simpson and Natalia Veselnitskaya
  • The meeting at the Trump Tower
  • Informants and surveillance (i.e. Mifsud, Downer, Halper, etc.)
  • The FISA warrants
  • DOJ and FBI leaks

Mueller Seizer Cycle
Image 9
: An illustration of Mueller’s typical Flutter Cycle.

The Flutter Cycle sequence was characterized by 2-5 rapid flutters of the eyelids and an upward eye roll, head, mouth, and accompanying tongue movements. This Flutter Cycle sequence seen in the left side of the collection in Image 10 (also, see 1:26:00 in the video) corresponds to questions by Rep Steve Chabot of Mueller’s investigation of the relationship between Glen Simpson, Natalia Veselnitskaya, and the latter’s visit to Trump Tower.

The same type of events were observed during other pointed inquires such as Rep Louie Gohmert’s challenging Mueller’s credibility due to his refusal to answer basic questions (see 1:33:30 in the video).

Mueller-Flutter-Cycle Mueller-Mouth quivering Mueller-Flutter-Cycle-2
Image 10
: (L-R) A sampling of three anomalies a complex facial flutter, lip twitching, and simple eye flutter sequences

Several other interesting anomalies that turned out to be repeating patterns in Mueller’s facial expression and composite sequences were:

  • Lip Twitching – Associated with microexpressions such as fear and surprise
  • Downward Head Nodding – Associated with other defensive posture the was triggered by breaking eye contact with the speaker
  • Flattened Mouth or Lips –  Associated with signs of frustration as in ‘I want to answer this question, but I really shouldn’t’
  • Prolonged Blinkless Stare Associated with angry and combative response to some question

Sampling of Mueller’s “Flutter Cycle” Events
Imager 11
: Samples of Mueller’s dozens of “flutter cycle” episodes during the Q&A

The Jolly Affable Mueller
Not all of Mueller’s testimony was marked by doom and gloom. On a number of occasions (mostly when talking to Democratic representatives), he showed himself to be charming, in high spirits, engaged, and animated. Mueller had no inhibitions about making remarks regarding the report’s failure to exonerate Trump and the possibility of persecuting Trump after he left office. He freely cited legal sources and DOJ procedures and protocols and provided detailed rationale for his team’s action and conclusions.

Mueller Fun and Jokes
Image 12: The suave, charming, engaged, and animated Mueller in action

Mueller’s predictable patterns of distress were almost always associated with ‘difficult’ questions on topics such as the role of Fusion GPS, spying on Trump, and Christopher Steele. Images 13 and 14 show a typical triggering events of a Flutter Cycles.

Mueller and Martha Roby-2
Image 13: Samples of Mueller’s Flutter Cycle episodes during Q&A session dealing with him leaking report details to the media

Mueller and Martha Roby
Image 14
: Sample of Mueller’s Flutter Cycle episodes during Q&A session dealing with separating the grand jury materials from the report

Analysis Results
Mueller’s body language and facial sentiment analysis shows high levels of discomfort and tension when discussing certain parts of the report. He exhibited many facial signs of distress that included:

  • Multiple Flutter cycles
  • Mouth quivering cycles
  • Self shooting and fidgeting behavior
  • Sudden breaking of eye contact
  • Rapid downward head movement
  • Hard swallowing
  • Tightening of the mouth and lips

I didn’t have a baseline for incidents where Mueller was being untruthful so I can’t explicitly call out potential incidents of lying during his testimony. However, the baseline of his normal conversational dynamics vs. the ones he exhibited show signs of clear distress which strongly suggest that at least from Mueller’s perspective, not all questions were equal and not all of his answers were factual.

Mueller distress patterns consistently overlapped with certain trigger topics and his verbal response to almost all of these interactions was a variation on the “I’m not going to…”.  He deviated from this pattern only a handful of times and actively engaged the questioner. One of these back alley knife fight sessions involved Rep Ben Cline’s stating that Andrew Weissmann was running a rogue investigation that was based on flawed legal theory that was overturned unanimously by the Supreme Court.

As the question was being asked, Mueller became defensive; he shifted uncomfortably in his chair, exhibited his Flutter Cycle, and replaced his poker face and laconic I-word response pattern with a passionate and verbose defense of Weissmann (see 3:19:40 in the video or sound file below). 

Image 15: Sample of one of Mueller’s distress patterns that includes his Flutter Cycle and uneasy shifting in his seat

Recording 1: Exchange between Rep Cline and Mueller about Weismann’s legal foundation of his obstruction of justice investigation

During this segment which lasted about two minutes, Mueller argued, spoke over Cline, and attempted several times to repeat his assertions about Weismann. This continued even after the subject of the questions changed to Obama’s culpability in Obstruction of Justice when he announced publicly that the HRC private email server did not pose any threat to national security. Mueller, without much difficulty, exhibited a decent mastery of the report’s content, cited specific areas in it that included the “lengthy discussion” and “lengthy dissertation”, and in general tried to rehabilitate himself and his team.

The overwhelming majority of Mueller’s testimony failed to illuminate any of the big questions about the DNC email hack, the genesis of the Steele Dossier, the DNC/Fusion GPS relationship with Russian state actors, and the 2016 surveillance on the Trump campaign. In fact, his answers raised even more questions about the real power behind the throne and R&R within the special counsel team.

If it is indeed the case, as Mueller confirmed in multiple answers, that no single individual on his vast team had intimate familiarly with the whole report, then who compiled the final version of the document?  Was this just a collation of multiple taskforce reports that were later combined into a single master? And if that is the case, who was the person that harmonized all the individual versions in order to make sure that the index, format, dates, people, places, reductions, and events were in sync?

Aaron Zebley
Image 16
: The Special Counsel Team and testimony attendees

It is noteworthy that Mueller continued to play the I-phrase card and refused to address any of the procedural questions about the compilation of the report. Even though, this information had little bearing on the report’s content and that there is nothing classified or proprietary about the way the DOJ writes and edits their documents.

Robert Mueller shows his card
Image 17: Mueller’s Trump Card

Even though Mueller attempted to obfuscate the report’s composition methods and authors, the writing style, document layout, context, and several other administrative clues strongly suggest that Andrew Weismann was the architect and Aaron Zebley was chief editor of the document. This is also likely the reason why Mueller insisted that Zebley be present by his side and be sworn in.

The evidence from the video analytics, speech dynamics, and the decision tree Mueller used to answer the questions (i.e. question objective vs. answer strategy) shows a decent mental agility and the ability to alternate between complete ‘radio silence’ and ‘singing like a canary’ on demand.

To those who still believe that Mueller was just a senile old man with little familiarity with the content of the report, consider the fact that his verbose answers show that he had a pretty good grasp of  the document. He also artfully navigated the many minefields in the report without blowing up a leg in the process. Some experts in the MSM have been suggesting that Mueller’s poor verbal performance and optics can be attributed to some form of cognitive impairment but this argument is inconsistent with his ability to effectively deliver the following:

  • Selectively discuss specific topics, most of which were prejudicial towards Trump
  • Answer questions that almost exclusively supported the impeachment narrative with certainty and conviction
  • Justify and emphasize specific areas in the report that exonerated his team from claims of bias towards Trump and instances of hostile conduct by FBI senior management and its agents (i.e. Comey, Strzok, Page, agent 2, and others)
  • Utilize the “I’m not going to…” strategy to answer any questions about the “insurance policy”
  • Refuse to address the media leaks that either came from him personally, his direct reports, or his team
  • Exhibited great mental agility and dexterity during the May 29th, 2019 Mueller news conference
  • Come up with over 198 different ways of not answering a direct question

The patterns identified by the analytics strongly suggest that all of Mueller’s behavioral stress patterns matched the typical anxiety profiles and signs of internal struggle that are exhibited by a deceptive suspect during an integration. For the first time in his long bureaucratic career, he found himself at the wrong side of the table with the bright lights in his face and a real possibility of being charged with perjury. For several hours, the fearless hunter became the pray and he clearly didn’t like the experience.

Contextually, the majority of his testimony turned out to be an underhanded attempt to use the Q&A session to justify, promote, and surreptitiously inject political narrative into the public hearing. None of this should come as a surprise as it is the same circular “impeach Trump” agenda that launched this investigation in the first place. At the end of the day, despite Mueller’s big title and god-like pedigree, he turned out to be just another DC power broker who apparently placed his bets on the losing presidential candidate.

Sample Report Pages
Image 18: Two pages (a total of 856 words) form the Mueller report dealing with George Papadopoulos being told by Joseph Mifsud about the Russian having “Dirt” on HRC.

Mueller’s elaborate 448 page report that took close to two years to complete, cost over 25 million dollars (that’s about $51K per page), involved 19 lawyers, 23 legal researchers, 40 FBI agents, 10 intelligence analysts, 7 forensic accountants, 25 other professional staff, and the unlimited resources of the DOJ, the State department, NSA, and the intelligence community, delivered an indefensible dud.

Reading the reports, you can’t but stop and appreciate the authors’ Kafkaesque sense of humor. In the example pages shown in Image 18, the report discusses the chain of transmission of the Russian “Dirt” from Joseph Mifsud, to Papadopoulos, to a mysteries western diplomat (Alexander Downer) who then informed the FBI, who naturally became alarmed and started this massive investigation. On the face of it, the document looks solid. It has all of the right trimmings, detailed claims, massive amount of footnotes, intelligence lingo, hush hush sources, and strategic reductions with alarming labels like “Harm to ongoing matter”. It is as convincing as a quality levitation magic act.

But, magic acts of levitating people are always predicated on the audience viewing the scene from a distance and through a carefully controlled field of view–which is exactly what the Mueller report and testimony turned out to be. It doesn’t work if you get a glimpse of the crane and the wires supporting the magician. Once you understand the mechanics of the magic, the awe gives way to a letdown.

You can test this premise by substituting any good magic act with the report and Mueller with any successful magician. Any question you ask the magician about the inner workings of his trick would be deflected using the exact same techniques Mueller used during his testimony. The most important rule in magic is NEVER tell the secret of the trick, just let the magic speak for itself.

The Mueller Levitation Magic
Image 19
: The incredible levitating magic act

What is ostensibly missing from these two magical pages in Image 18 is that the source of the “Dirt” was none other than Stefan Halper, a paid FBI informant who billed (using DUNS # 078459148) the Federal Government about $656,535 for his services. By the time you factor Halper and his harem of young female assistants, Mifsud and his life of luxury at his safe house, Downer’s expenses, and at least 11 other IC, CI, and State Department assets that supported Halper in fattening Papadopoulos before he was shish-ka-bobbed by bob, the cost of these two pages to the US taxpayer was probably upwards of a million dollars. So, to those of you who still think that majoring in contemporary English fiction won’t pay the bills, it clearly can! After all, what other line of work pays $1168 per word?

Stefan Halper Payment
Image 20: Stefan Halper’s government payment record for service provided to the DoD and DOJ from 2016-2018

Summum bonum
I have difficulty finding solace in Mueller’s bragging about the higher good from his recovery of about $40 million from the Paul Manafort persecution. I’m also not sure if we should laugh or cry about the concept of the DOJ becoming a profit center. The problem with the DOJ acting as a collection agency that recovers the cost of prosecution from its targets is the political nature of selecting their next victim. Each one of us including the Honorable Mr. Mueller has something in his past, present, or future that warrants jail time and property seizure. With over 3000 federal and thousands more state laws on the books, we are all guilty of some misdemeanor or a felony. Who in the DOJ then, gets to make the decisions about who/why to persecute and the ultimate greater good? Is it going to be one of the dozens of high power attorneys that regularly walk through the DOJ revolving doors to personally enrich themselves by constantly hopping between government gigs and private practice?

The problem with the whole Manafort affair is that if he was so thoroughly corrupt in 2007, then why didn’t Mueller investigate him earlier during his 11 year tenure as the director of the FBI. Why did he wait until 2018 to bring these charges?:

“…crimes arising out of payments he received from the Ukrainian government before and during the tenure of President Viktor Yanukovych.”

After all, the DOJ, FBI, and the IC had a supersized file on Manafort going back to 2007, so why wait for all these years?

Yanukovych Mueller and Manafort
Image 21
: The Triumvirate or Threesome (depending on your view)

Mark Twain once wrote that:

“Anybody can tell lies: there is no merit in a mere lie, [for a good deceit] it must possess art, it must exhibit a splendid & plausible & convincing probability; that is to say, it must be powerfully calculated to deceive.” 

Mueller’s report doesn’t come close to Twain’s definition of deceptive genius, but it does have a certain kitschy synthetic Disneyland feel to it. In many ways its similar to another secretive report, the Protocols of the Elders of Zion. Both, share the same conspiratorial elements, treachery, mysterious meetings, made-up events and agendas, secret societies, informants, and intrigue.

All of this hush-hush secret agent man stuff in the report seems very mysterious, but at its core, it’s really a simple criminal matter. If you’ve ever been a juror on a criminal trial, you should be familiar with the routine. If you haven’t, it goes a s follows:

  • The prosecution and the defense present their case with an opening statement
  • Both show evidence and present witnesses
  • Both cross-examine witnesses 
  • Each side delivers their closing arguments
  • The jury goes into deliberation and comes up with a verdict

In any normal criminal trial in the US, they typically follow the Federal Rules of Evidence, there is no such thing as secret testimony that can’t be verified or evidence that can’t be shown to the jury. If the DA doesn’t want to expose his sources/methods then they get excluded from trial. If witnesses can’t be cross-examined, their testimony is inadmissible. It’s as simple as that. And all of this procedural stuff doesn’t even address the issues with Mueller whitewashing the existence of several rogue and biased agents/attorneys on his own team.

Gowdy vs. FBI
Image 22
: Rep Gowdy and DOJ IG Horowitz Q&A session regarding Peter Strzok’s and Lisa Page’s involvement in the Mueller and HRC Email investigations

So, no, I wouldn’t classify Mueller’s report as a deceptive masterpiece, I would rather categorize it as more of a ‘true story’ type of a tale, blunderingly delivered by a DC swamp-raised shrimp.

The Little Shrimp from DC

The True Story
It’s true…
It’s true and the other thing is                
my sister had a baby
and I took it over because she passed away.             

and then the baby lost its legs and its arms                  
and now it’s nothing but a stump
but I still take care of it with my wife
and it’s growing and it’s fairly happy.               

But it’s difficult ’cause I’ve been working
a second shift at the factory to put food on the table,                
but all the love I see in that little
guy’s face makes it worth it in the end.

True story!

The Mueller Testimony – Full Transcript
The Mueller Report – Full Report
Human Resource Exploitation Training – Interrogation Manual

Toris, C., & DePaulo, B. M. (1984) – Effects of actual deception and suspiciousness of deception on interpersonal perceptions 

MG Frank – ‎1997 – The ability to detect deceit 

Analytical thinking – The analytical thinking algorithm was based on the results from a series of studies by: Pennebaker, Chung, Frazee, Lavergne, and Beaver (2014.

Clout – Clout refers to the relative social status, confidence, or leadership that people display through their writing or talking. The algorithm was based on the results from a series of studies by: Kacewicz, Pennebaker, Davis, Jeon, & Graesser, 2013.

Authenticity – The algorithm for authenticity detection was based on a series of studies where people were induced to be honest and deceptive. See Newman, Pennebaker, Berry, & Richards, 2003 and Textual Models of Deception to Interrogation Settings.

Emotional tone – The positive emotion and negative emotion tone algorithms were based on the results from a study by: Cohn, Mehl, & Pennebaker, 2004. See Linguistic Markers of Psychological State throughMedia Interviews: John Kerry and John Edwardsin 2004, Al Gore in 2000

Copyright 2019 Yaacov Apelbaum, All Rights Reserved.

Do Clothes Make the Woman?

Alexandria Ocasio-Cortez with her plaisters

“Clothes make the man. Naked people have little or no influence on society”

Some attribute the source of this quote to Mark Twain (1835–1910), but he wasn’t the first to observe the human propensity to dress for the occasion.

Twain, likely quoted William Shakespeare (1599), who wrote in The Tragedy of Hamlet:

“The apparel oft proclaims the man.”

Shakespeare, in turn may have cited Peter Idley (1474), who wrote in his Instructions to his Son:

“Ffor clothyng oft maketh man.”

Idley, in turn probably quoted Erasmus (1500-1508), who wrote in his Collectanea Adagiorum and Adagiorum Chiliades The Encyclopedia of Proverbs (Adagia 3.1.60):

vestis virum facit”– clothes make the man

Erasmus, in turn, quoted the Roman poet Quintilian’s (35 CE–100 CE), who wrote in his Institutions (oratory–8 pr. 20):

“To dress within the formal limits and with an air gives men, as the Greek line testifies, authority.”

Quintilian, in turn, likely paraphrased Homer (484 CE–425 CE), who wrote in the Odyssey  (6.29–30):

“From these things [clothes and personal care], you may be sure, men get a good report”

My take on the concept of “Clothes make the man” is that the dress code should match the  occasion. From a sampling of 30 outfits from AOC’s vast wardrobe catalog, it looks like Cortez, the high priestess of the progressive movement and the friend of the little workingman/woman only leaves the house in ± $1.5K outfits.

Alexandria Ocasio Cortez Border Accessories
Image 1: He/She comrade Alexandria Ocasio-Cortez’s trendy white outfit and the horror of the experience of standing in front of an empty parking lot at the Tornillo CBP center.

AOC The Outfit
Image 2
: He/She comrade Alexandria Ocasio-Cortez’s working class outfit made up of a Blazer with Pants by Gabriela Hearst, Victoria Secret undergarments, and Manolo Blahnik pumps.

This obviously begs the question of how is it that a former waitress/bartender who only earned ± $27K in 2017 (and still owes about $50K in student loans) managed in less than a year to amass over $50K in clothes, shoes, bags, and bling?

AOC What's a Girl to Wear
Image 3
: He/She comrade Alexandria Ocasio-Cortez’s and her working class wardrobe

Yes, clothes can help enhance a person, but without the exercise of discretion and independent judgment, they amount to little more than putting lipstick on a pig.  Or as Proverbs 11:22 puts it:

As a ring of gold in a swine’s snout,
So is a fair woman without discretion.

As a ring of gold in a swine's snout,
Image 4: Self-explanatory

Copyright 2019 Yaacov Apelbaum, All Rights Reserved.

William Shakespeare – Hamlet
Peter Idley’s – Instructions to his Son dated 
Erasmus of Rotterdam – Adagiorum Chiliades 
Quintilian – Institutes of oratory; or, Education of an orator
Homer – The Odyssey
Alexander Atkins “Clothes Make the Man”

It’s All About Climate Change, Man!

Satyricon 2019

This year’s Google Camp will be hosting a summit with the biggest names in show biz, politics, high-tech, music, and fashion. It will include notables such as former President ObamaLeonardo DiCaprio, Prince Harry, Orlando Bloom, Harry Styles, Bradley Cooper, Nick Jonas, Priyanka Chopra, Gayle King, Mark Zuckerberg, Diane von Furstenberg, Katy Perry, and many others. 

The event is taking place at the 5-star Verdure resort in Sicily. Due to the high prestige and number of guests, the hotel is fully booked, and room prices start at $930 per night.

The all-expenses-paid, three-day event hosted by Google, will cost about $20 million which comes to about $33K per person, per day. The estimated 120-200 participants will discuss urgent global issues such as on-line user data privacy, freedom of speech, and global warming. The main focus of the event will be climate change, which, according to several of the attending subject-matter experts is the biggest threat to the world and our future generations.

The guests will be arriving at the event over the next 12 hours in 116 private jets and the world’s largest private megayachts. The estimated combined carbon footprint output from the resort activity and travel to and from the three-day event will be equivalent to the yearly carbon production of over 900K US households.

A Flight and a Dinner
Image 1: A dinner and a show at the Temple of Hera and private jets delivering the summit attendees

In the spirit of openness, Google went the extra mile to keep all resort activity a secret—all support, hotel, and security staff signed restrictive non-disclosure agreements prohibiting them from discussing or taking any images of the events and participants.

And yes, as the video analytics reveals, the environmentally-conscious guests are using plastic straws for sipping their very expensive, Google sponsored cocktails.

It's All About the Climate Man
Image 2: The Carbon footprint of David Geffen and Katy Perry, two of the over 140 Google climate change summit attendees

But WAIT! What did I just hear? Google doesn’t get its 260-400 million watts of energy (2-4 percent of the world’s electricity) from unicorn powered wind farms and eco-friendly solar panels? And it’s responsible for 1.5-3 million metric tons of carbon dioxide emissions every year, which is about 20%-40% of the internet carbon footprint?

I’m Shocked, SHOCKED To Find There’s Gambling in the Casino!

To the legions of the woke, if you haven’t caught up yet, you are looking at corporate greed incarnate. Where environmental disasters like the BP Deepwater Horizon or Union Carbide/Dow Bhopal were terrible but isolated events that could be attributed to human error or gross negligence, Google’s entire business model is based on a carefully executed global human and environmental exploitation.

Aren’t you a bit curious how is it that social media giants the likes of Google, Twitter, and Facebook spend hundreds of millions of dollars on software development, hardware, and pay astronomical electric bills for their worldwide datacenter operations, and still make billions in profit–all while offering these services for ‘free’?

At the end of the day, this whole climate change summit thingy is just a cynical PR move to hide the fact that Google can’t burn fossil fuel fast enough to power its worldwide data center expansion–which since 2016 have been working overtime to promote fake news via their ad-sense cash cow, while at the same time, destroying whatever little is left of privacy and suppressing free speech.

So next time Google/FB/Twitter or a celebrity tells you just how important climate change is and asks you to donate to their foundation, tell them that you are open to learning more about the carbon footprint over dinner at the all-expense-paid outing on their private jet or megayacht.

Green private jets? Don’t make me laugh
Daily emissions of cruise ships same as one million cars
Google accounts for about 40% of the internet’s carbon footprint
Google isn’t actually tackling ‘fake news’ content on its ad network
The more outrageous, the better: How ad-sense makes money for fake news sites

Copyright 2019 Yaacov Apelbaum, All Rights Reserved.

One Thousand and One Nights and Ilhan Omar’s Biographical Engineering

Ilhan Omar The Witch Princess

Despite numerous interviews, media write-ups, and multiple biographical sources, Ilhan Abdullahi Omar AKA “Ilhan Nur Said Elmi” remains an enigma. As strange as it sounds, there is almost no verifiable information about her nor her family in the public domain. This is bordering on the fantastic considering the fact that she is a ±34 year old (she has no birth certificate) sitting congresswoman. In an era where comprehensive digital background searches can be executed with a single mouse click, Omar’s fluid and ever shifting identity and invisible past are a puzzle.

The majority of the biographical information that she disclosed has multiple non-reconcilable versions. This includes significant inconsistencies about basic facts such as:

  • Her family’s actual size
  • Their real names
  • The family wealth and real-estate ownership in the US and Somalia
  • Their birth dates
  • Details about her brother/husband’s identity
  • Her father’s political role in the dictatorship of Said Berra
  • Their residence in Baidoa and Mogadishu
  • Any evidence that the family spent years in the Dadaab refugee camp instead of the resort city of Mombasa
  • The real reason for their escape from Somalia

The problem with Omar’s exceptionally poor recollection of facts and propensity for embellishments isn’t limited to the period between 1990-2011. Just a few weeks ago, on May 28, Omar delivered the following speech in the Richfield high school about her first hand experience with a “sweet old African lady”:

“sweet, old . . . African American lady” who had been arrested for stealing a $2 loaf of bread to feed her “starving 5-year-old granddaughter.”

After spending the weekend in jail, the woman was led into the courtroom and fined $80—a penalty she couldn’t pay.  I couldn’t control my emotions, because I couldn’t understand how a roomful of educated adults could do something so unjust.”

If you haven’t noticed, this plot-line suspiciously resembles Jean Valjean’s crime in Victor Hugo’s Les Miserables.

As with most of her claims and recollections, this story too is unverifiable. She doesn’t remember where or when it took place, what court handled the case, or who the “sweet old. . . African American lady” was.

Omar May 28 Richfield High School Visit
Image 1: Ilhan Omar delivering the speech about the “sweet, old . . . African American lady” who had been arrested for stealing a $2 loaf of bread to feed her “starving 5-year-old granddaughter

The fluidity of the Oma’rs background suggests that most of the details that we know about her have been engineered. It is also clear that over the past 27 years, core components of the Omar family dossier have gone through multiple revisions at the hands of someone who has a decent grasp of covert intelligence operations.

For example, between Ilhan, her father, current husband, and one of her sisters, they have 32 name variations that are being used interchangeably in various official registries.

Omar Ilhan
Ilhan Omar
Ilham Umar
Omar Ihan
Omar Ijhan
Ilham Omar
Ilham A Omar
Ilhan Abdullahi Omar

Abdullahi O Mohamed
Nur Mohamed
Nur Omar Mohamed
Nur Said Elmi Mohamed

Current Husband
Ahmed A Hirsi
Ahmed Aden
Ahmed Hirsi
Ahmed Abdisalan Hirsi
Ahmed A Aden
Ahmed Hrisri
Ahmed Hersi
Ahmed Haji
Ahmed Hassan

Sister (US Based)
Sahra Abdullahi Omar
Noor Sahra
Omar Sahra
Sahra Noor
Sahra Omar
Sahra A Omar
Sahra Abdullahi Omar
Sara Omar
Sarah Omar
Kadra Omar

Ilhan’s perpetual reenactment of the comedy of errors of family name/identity mismatches is not an English as a second language issue. The same pattern appears when she communicates in her native Somali tongue. During her 2016 visit to Somalia and a meeting with president Mohamed Hassan Sheikh, she told the local media, in Somali, that the man by her side was her husband Ahmed Elmi. Running face recognition on the images taken by a Somali state media service photographer shows that the person accompanying her was in fact Ahmed Hirsi.

Official Photos
Image 2
: Photographing Ilhan Omar and her husband Ahmed Hirsi visit with the Somali president. Images taken by a Somali state media service photographer.

As can be seen in Image 3, shortly after her marriage issue surfaced the photos in the article were deleted from the server. I contacted “Hassan Istiila”, the author of the article and asked him why he removed the images, he denied doing so himself (see email exchange in the references). The post edit history and server logs show that an admin user by the name of Farsamada AKA Liibaan Mohamoud Yusuf Nabiil was the one who removed the images from the WP CMS used by Radio Dalsan. It also turns out that Farsamada is a dedicated Ilhan Omar supporter/promoter in Somalia (see Image 5).

2016 Ilhan Breaking News
Image 3: The Mogadishu Radio Dalsan article about Ilhan Omar’s 2016 visit to Somalia and her meeting with the Somali president Mohamed Hassan Sheikh. The article identifies her husband as Ahmed Elmi but in fact it is Ahmed Hirsi. 

2016 Omar Somalia Visit with Elmi
Image 4
: The Mogadishu Radio Dalsan 2016 article with some of the recovered images showing that the person accompanying Ilhan Omar was in fact Ahmed Hirsi traveling under the name Ahmed Elmi (Ilhan Omar only divorced Ahmed Elmi in 2017).

Mr. Farsamada Omar's Media Muscle
Image 5
: Farsamada AKA Liibaan Mohamoud Yusuf Nabiil, the Somali news editor who purged Ilhan Omar’s images from the Mugadisu Radio Dalsan article that identified Ahmed Hirsi as Ahmed Elmi. 

Omar’s 2016 trip to Somalia also raises questions about potential FARA violations, her funding sources, and the actual purpose of her visit with the Somali president. From her itinerary it is clear that this was an expensive state function that took place two years before Ilhan was elected to Congress. So who exactly paid for the visit? And why did the Somali president “requested her presence” at his villa? Unless, of course, Omar was there on a crypto-Elmi family financial/political interest expedition or as a US/Somali power broker.

Another good illustration for this steady stream of name/identity shell games and biographical revisionism is her famous ‘militia assassination’ story. This tale which forms the foundation of Ilhan’s Somalia fairytale escape narrative (and likely the basis for the US asylum application) details a traumatic night in Mogadishu when her family’s secure compound was attacked by an unknown rebel militia who tried to “assassinate” her entire family.

The following are two versions of this story as told by Omar in 2016. The first version is from her interview with Cory Zurowski from CityPages News. The second version—which she told a month later—is from an Omar propaganda featurette that was produced by Daniel Cummings.

1. Ilhan Omar’s Improbable Journey from Refugee Camp to Minnesota Legislature
CityPages News – Published 26 October, 2016

“Nighttime fell as about 20 people milled about the compound in Mogadishu, the Somali capital. Bad noises outside announced unwelcome visitors. Men with big guns demanded to be let in. The group tried to bust down the front door, but it was unbreakable. Omar and her family fell to the floor moments before the militiamen let go a staccato of gunfire. Once they were satisfied with the evening’s damage, the attackers left.

Everyone survived. Omar hasn’t forgotten the sight of bullet pockmarks in the building’s cinderblock walls.

Shellshock turned to grief for Omar’s grandpa. “That was a hard realization for my grandfather, that our family was no longer welcome,” she says. “Even after the attack, he struggled with this new reality.”

Days later, they put their familiar world in the rearview mirror. They split into groups, 20 people in total fleeing. Omar’s headed for the coast where Abukar [grandfather] had connections. Alongside her father, she hopped a plane for Kenya.”

2. Ilhan Omar and The New American Dream
Movie Featurette – Published 15 November, 2016 

[Go to 55:14 in the recording to hear the description below]

“Think my earliest memory of when the war actually felt real was waking up you know midnight… to militiaman surrounding our house. My aunt and older sister having a conversation through the window, sort of a negotiation with them because these were classmates of theirs, umm… and… and…and… negotiating them to sort of back down… and umm…and trying to break into our house and assassinate us.

We ended up surviving that night then my grandfather said that we like had to leave. We all left thinking that we’ll come back…that umm… you know in a few months the war would end, so we sort of fled I think like at 4 AM.

And walking through this… this…streets and actually, umm… walking over dead bodies and seeing debris and buildings that have fallen apart umm…sort of land marks of like hotels that you know, umm… that had movie theaters and all of this that we used to go by and… and… see that they no longer were standing tall…”

Both of these versions have major structural flaws. This includes discrepancies in the timeline, the sequence of events, the number of individuals involved, and the plot line. As can be seen from the table below, there are embellishment and missing elements in each version. For example, the claim that the militia opened fire at the house vs. that they retreated peacefully after the aunt and sister negotiated with them or that the militiaman were classmates of her aunt and or sister.



October 26

November 15


20 men in the militia



Compound in Mogadishu




Militia outside the compound



Militia surrounded compound



Aunt & older sister talking to militia



Militia classmates of aunt and sister



Man with big guns



Trying to bust down the door



Militia tried to assassinate them



House door was unbreakable



Omar and family fell to the ground



Militia opens fire at the house



Memory of bullet pockmarks on wall



Family not welcome in Somalia



20 family members fleeing



Catching a flight to Kenya



Dead bodies & destroyed buildings



Grandfather said we had to leave



Table 1: A sample of the variations in several tales of the Somali rebel militia storming the Omar compound in Mogadishu

Both versions of the story make little sense, especially the preposterous assertions Omar makes about the ‘magic door’ that could not be breached and how her family, consisting of 20 individuals (who weren’t connected to the brutal dictator Said Berra) managed on short notice to get hard currency, valid passports, visas, airline tickets, transportation to the airport, and a safe passage through multiple checkpoints controlled by Mohamed Farah Aideed’s militia to catch an international flight to Kenya.

Following this and many other Omar biographical dichotomies, I decided to examine her public statements regarding her family history. For any other US elected official, these types of gaping holes in a personal and family history would normally trigger a lynch mob of angry MSM investigative reporters. But as it has been already demonstrated, Congresswoman Omar seems to have managed to somehow acquire the ring of Gyges and will not answer questions on these subjects because she deems them too offensive and an invasion of her privacy. 

Two Poor Somali Refugees
Image 6: Somali refugees and their political affiliation with the regime of the Somali dictator Mohammed Siad Barre

So instead of hoping that someone will finally wake up and bring her in for questioning, I figured why not do the next best thing–take some of her video appearances and run them through a software based polygraph analysis.

The Evaluation Process
I wanted to conduct the evaluation in an impartial manner and eliminate the Christine Blasey Ford effect and the observer bias. To achieve this, I’ve only used movies of Omar that had her speaking clearly and audibly and featured a close-up view of her face at a sufficient resolution. This was important for facial feature extraction. I also automated the image optimization and evaluation process.

In assembling the virtual polygraph system, I used several Artificial Intelligence platforms to perform the speech and facial expression evaluation. For the facial and speech analytics I used:

For the speech analysis I used several algorithms to identify conversational dynamics and the detection of deceit through functions and patterns such as:

  • Usage of filler words like “Umm”, “and”,  Like”
  • Cut out contractions such as using “did not” rather than “didn’t
  • Increase/decrease in speech speed, pitch, and tone
  • Pausing frequently
  • Prolonging word vocalization to buy time
  • Repeating or rephrasing questions
  • Stuttering
  • Voice pitch changes

I calibrated the facial expression engine and established a baseline using several hours of Omar’s speeches on a variety of ‘neutral’ subjects. I then established her ‘truth’ baseline using over 1200 factual statements (i.e. statement known to be true). I also supplemented her baseline facial expressions with catalogue of over 230 combinations of her customized Microexpression.

Following the creation of a unique database of her facial expressions, I proceeded with the ML training using her unique data sets. The whole training process took about 32 hours.

After the training was completed, I fed several movies into the engine and evaluated the accuracy of the system performance in terms of false detection and missed detection using a manual ground truth evaluation. During that evaluation, I examined several hundred individual video frames, correlating them to what Omar was saying and the AI detections of face expression.

The results of the ground truth successfully confirmed the accuracy of the training data and the precision of the speech and facial expression algorithms.

Ilhan Omar Sample Analysis
Image 7: Sample of the facial analysis results for one video frame out of about 120K frames analyzed.

Ilhan Omar Interview Facial Set
Image 8: Sample of a few of the over 4000 frames analyzed from the ‘Militia Assassination’ sequence

Image 9: Sample of a few of Omar’s Microexpressions such as contempt

Blinking and soothing
Image 10: Sample images showing rapid eye flickering and fidgeting (soothing behavior) correlated to the ‘Militia Assassination’ sequence

Ilhan Omar High ResImage 11: Sample of an image that has gone through the optimization and enhancement process prior to using it in facial analysis

Analysis Results
The textual analysis shows that when Omar is talking about the gray parts of her biography her first person singular pronouns decrease, her negative emotion words increase, and her complex word choice decrease. This is most likely attributed to an increased cognitive overload that is associated with creative thinking. She also uses many more action verbs then average in these segments which suggests that she want’s to keep the story moving and discourage a challenge on the part of the listener.

The speech and facial sentiment analysis strongly suggests that Omar was deceitful when discussing her family Mogadishu history. She exhibited all of the typical facial and body mechanics indicators for dishonesty including:

  • Stresses
  • Evasiveness
  • Contempt
  • Nervousness
  • Suspicion
  • Fear
  • Disgust

Omar’s analytic ‘lie’ score consistently exceeds her ‘truth’ baseline by 30%-80%. Her patterns also exhibit many indications of a compulsive fibber. She is a a great storyteller; her tales tend to be very detailed and colorful and she seems very convincing. She is a natural performer, she is eloquent, and she knows how to successfully engage her audience. When providing historical information about her family, she never does so matter-of-factly (the way most people retell their family histories). Rather, Omar’s narrative is always epic and geared at acquiring the listener’s admiration and sympathy.

The following are a few examples that suggest that she could be either a pathological liar or is working with well written PR scripts:

  • She often talks about experiences and accomplishments in which she and her family play heroic parts
  • She is often a victim (of physical violence) in her stories and is habitually looking for sympathy and protection
  • She is always brave, virtuous, better then her adversaries, and beyond reproach
  • Her stories tend to be very detailed but lack any verifiable data 
  • She responds elaborately and quickly to questions but her responses are always vague and almost never answer the question
  • She has different versions of the same story and uses all of these versions at times
  • Confirmed facts about her and family don’t support her version of the events

Omar Sentiment Chart
Image 12
: Omar’s sentiment chart showing her emotional state when discussing the ‘Militia Assassination’ event

Omar habitually exhibits this pattern of evasiveness and embellishment in all of her narratives. For example, we know from multiple interviews that the count of her siblings changes and varies at any one time from 3-7. This begs the question of what her family’s real story is and why are they so reluctant to disclose it.

Omar is a naturalized US citizen with a very ambiguous/suspicious background that doesn’t seem to match her official biography. As a public figure and congresswoman, she has or will have access to sensitive and classified state secrets, as such, it may not be a bad idea for DHS and INS to run her record in order to eliminate any potential security breaches downstream.

Anyone working for any three letter agency must go through a background screening and a detailed character evaluation. Some even are mandated to take periodic polygraph tests. There is no reason why a top lawmaker like Ilhan should be exempt. If her biographical details match her official story, then she should get the royal stamp of approval and be done with all the speculation. If it does not, then there should be full public disclosure of the details and a congressional hearing regarding the discrepancies.

In 1995, when Ilhan was about 12, she finally got her wish and arrived to New York. While being driven to her new suburban home in Arlington Virginia she recalled the following memory: 

“Staring out a car window, felt ripped off. This wasn’t the same country as the one showed to her on the INS orientation videos. Instead, it was a headache of car horns and homelessness, an eyesore of trash and graffiti.”

The Hirsi Omar Enterprise
Image 13: Parts of the Hirsi Omar enterprise in Minnesota

Ilhan Omar and Tim MynettImage 14: Ilhan Omar and Tim Mynett her strategist at a CAIR event hosted by Hassan Shibly, Ilhan holding hands with Mynett, dinning out with him, and spending a lot of ‘personal’ time together while she is still married to Ahmed Hersi.

Ilhan Omar Payments to Tim Maynett
Image 15: Ilhan Omar’s 2018-19 financial records showing over $250K in payments to Tim Mynett’s E Street Group LLC for his fundraising and consulting services.

Ilhan Omar Fine Italian Dinning with BF Ilhan Caffe Pinguini Confirmation
Image 16
: March 23rd-24th 2019, Ilhan Omar and Tim Mynett holding hands and dinning at the Café Penguini Italian restaurant in Playa Del Ray.

Hassan Shibly Guns and CAIRImage 17: Ilhan Omar and her friend Hassan Shibly, the CAIR-Florida Chief Executive Director. Shibly is the one who wrote Ilhan’s speech at the CAIR fund raiser in LA where she stated that “Some people did something”.  Shilby is on the US Terror Watchlist, CAIR and it’s members are also designated a terrorist organization by the UAE government.

From their financial enterprise, it seems that the Omars were taking careful notes while watching the INS movie about the freedom of religion and economic opportunities in America. It also looks like they have been binging on Dallas and Beverly Hills, 90210 style shows. It’s a shame, they skipped the whole chapter on the rule of law. 

وَيْلٌ لِّكُلِّ أَفَّاكٍ أَثِيمٍ
A fake refugee hatched out a plan
And perched on it like a crafty hen:

This self righteous adulteress maid,
Has made an art of this charade.

A Congresswoman on a hate crusade
Should carefully watch her vile tirade:
Appreciate the kindness done to you,
forget not you
owe your life to a Jew.

No matter how powerful this liar is
Allah is mightier than she is:
He raises up the righteous to bliss,
And brings the deceivers to their knees.

Email exchange with Hassan Istiila

From: Hassan Istiila <>
Sent: Sunday, August 04, 2019 5:54 AM
To: <
Subject: Re: Question about one of your articles

Hey YA,

Thank you for reaching out, i left the Radio Dalsan a year ago so i don’t know why the images was being removed and i don’t have the images now.

Hassan Istiila

Former Editor, Former freelancer with News Agencies.
Blogger, social media trainer, media consultant, researcher and Human Rights Activist.


On Fri, Aug 2, 2019 at 7:22 AM Y A <Apelbaum> wrote:

Hi Mr. Istiila,

I am writing an article on Ilhan Omar’s visits to Somalia in 2016 referencing your article below and was hoping that you can help me.

I’ve noticed that five images that came with the article were just recently removed. Would you be able to share these images?  Also, any reason why you identified her husband in the article as Ahmed Elmi instead of Ahmed Hirsi?

Best regards,



Copyright 2019 Yaacov Apelbaum, All Rights Reserved.

Self vs. Public Perception and the USS Becuna

Image 1: Digital remastering of the original artwork by Y. Lovell located at the torpedo room of the US Navy Submarine Becuna (SS-319).

USS Becuna USS-319
Image 2: Commemorative postal covers marking the keel laying on 29 April 1943 and her launch on 30 January 1944 at the Electric Boat Company, Groton, CT. Starboard view of the USS Becuna entering Pearl Harbor, circa 1944.

SS Becuna entering Pearl Harbor 1944-45
Image 3: USS Becuna returning from a patrol to Pearl Harbor 1944-45

SS Becuna at Pearl Harbor 1944-45
Image 4:
USS Becuna docked at Pearl Harbor 1944-45

SS Becuna resupplying
Image 5: USS Becuna conducting a high line transfer

Becuna SS319 Interior
Image 6:
USS Becuna Interior (L-R) Control room, maneuvering room, engine room (with the two engines “Huff” and “Puff”), torpedo room, and the galley

A Submarine
(From an edition of The Dolphin, the SUBASE NEW LONDON newspaper, April 4, 1925)

Born in the shops of the Devil,
Designed in the brains of a fiend;
Filled with acid and crude oil,
And christened “A Submarine”.

The poets send in their ditties,
Of Battleships spick and clean;
But never a word in their columns,
Do you see of a submarine.

I’ll try and depict our story,
In a very laconic way;
Please have patience to listen,
Until I have finished my say.

We eat where’re we can find it,
And sleep hanging up on the hooks;
Conditions under which we’re existing,
Are never published in books.

Life on these boats is obnoxious,
And that is using mild terms;
We are never bothered by sickness,
There isn’t any room for germs.

We are never troubled with varmints,
There are things even a cockroach can’t stand.           
And any self-respecting rodent,
Quick as possible beats it for land.

And that little one dollar per dive,
We receive to submerge out of sight;
Is often earned more than double,
By charging batteries at night.

And that extra compensation,
We receive on boats like these;
We never really get at all,
It’s spent on soap and dungarees.

Machinists get soaked in fuel oil,
Electricians in H2SO4;
Gunnersmates with 600W,
And torpedo slush galore.

When we come into the Navy Yard,
We are looked upon with disgrace;
And they make out some new regulations,
To fit our particular case.

Now all you Battleship sailors,
When you are feelin’’ disgruntled and mean;
Just pack your bag and hammock,
And go to “A Submarine”

References and Sources
Images courtesy of: Darryl L. Baker, Jack Treutle (of blessed memory), and John Hummel, (USN) retired
Submarine Photo Archive – USS Becuna (SS-319) (AGSS-319)


Copyright 2019 Yaacov Apelbaum, All Rights Reserved.

Military Romantic Scams – The Theory and Practice

The US Army Captain Doctor who Broke my Heart

Romantic scams a la Casanova style have been around forever, but the Military Romantic scam variant has only taken off since the advent of social media and on-line dating. According to a 2018 BBB Online Romance Scams study, since 2015 this form of cybercrime has become a 1 billion dollar industry in the US alone. The lifecycle of this scam is well documented and understood, but the low-level details and logistics have never been made public. Little is known about their operations, networks, organizational structure, payment collection and clearance methods, the technologies they use, and their relationship with their domestic financial institutions, law enforcement, and politicians. The public’s perception of cyber scammers is that of modern day pirates; an amorphous buccaneer entity shrouded in mystery that operates from somewhere overseas with what appears to be complete impunity from prosecution and justice.

The Victims
Two victims of military romance scams (L-R) Kate Roberts and Esther Ortiz-Rodeghero.

The impact of military romantic scams on the victims who are typically older women living on a fixed income is devastating. Some end up going into debt to pay the scammers; others lose their entire life savings. To add insult to injury, many of the victims continue to be blackmailed and forced into committing crimes like trafficking counterbid long after they discover that they have been defrauded. This can happen even if they try to terminate their communications with the scammer. Due to the international nature of these scams and the social stigma they carry, most victims have no legal recourse nor a chance of ever recovering their losses.

So in the spirit of Proverbs 31:8-9 “Speak up for those who cannot speak for themselves…”  I’ll try to pull the curtain back on one military romantic scam and do a deep dive into its inner workings and its perpetrators.

Military Romantic Scams 101
At its bare bones, the military romance scam has several variants, all of which revolve around repurposing images of some serviceman or woman and using them to create a fake profile on a social media or a dating site, then reaching out to a victim and defrauding them.

As can be seen from Image 1 and Table 1, a single set of stolen images can become a source of dozens of fake identities and over 900 concurrently running scams. 

The Many Faces of Richard Canon  Military Personal Identity Theft
Image 1: (L-R) The 49 personalities of Richard Canon and 12 other stolen identities used in concurrent romantic scams.


Fake Name

Running Scam Count


Richard Williams



Burks Richard



Richard Tonsom



Canon Hendryx



Jimmy Bernett



Richard Canon Miller



Johnson Smith



Richard Canon



Canon Richard



Thomas Lavallee (Richard)



Richard Thomas



Thomas Rick



Jeff Mathew



Thomas Hunter Richard



Charles Richard



Seth Berman



Romos Johnson



Mark Brandon



Richard Wilson



Mata James



Thierry Pienaar



Sherman Massingale



Brain Kobi



Elijah Grayson



Richard Canon Martin



Richard Chocktoot Kenneth



Mark Richard



Richard Smiff



Ballantyne Richard



Burks Richardws



Richards Jack Hanan



Richard Billy



Randy Moore



Richard Steven Nelly



William David Mark



Larry Brent Richards



Cannon Richard



Richie Arrow



Sivewright Richard



Richard Thorpe



Richard E Garlock



Richard Clark



Froylan Richard



Canon J Richard



Richard C Brain



Canon M Richard



Ryann Camryn



Cannoon Richard


Total scams for image


Table1: One of the hundreds of concurrent scams using the same image with different name variations

Once the fake on-line profile is created, the scammer will then pursue certain victim demographics and concentrate their efforts on a previously proven cross-section of susceptible individuals—for example, a divorced or widowed woman over 50.

After the initial contact is established with a victim, the scammer will then engage in a trust-building campaign that can take weeks or months. As part of this process, the scammers will transmit to the victim a wide range of collateral that can include a fake military ID, service photographs, additional social network images, and even address and property information about their fake residence.

The emails, text messages, and step-by-step logic used in these exploits are all based on hundreds of previously tested and successful scams. This makes their decision trees robust and the playbooks plausible and effective. Also, because the operator doesn’t have to waste any time on trial and error approaches, improvisation, or real-time content creation, they can effectively run dozens of concurrent scams by just picking up some template-based material, such as handwritten love letters, and minimally customize them to the specific victim.

Fake Love Signs
Raymond Ward Fake ID
Image 2: An example of template based collateral ready for customization and a fake military ID with typical errors that include: wrong service branch, rank mismatch, capitalization errors, mixed font use, inconsistent letter bolding, a mixture of imperial and metric units, and reuse of DOG TAG numbers from other fake IDs.

Any compromising information such as sexually explicit material, family, legal, or medical information the victim shares about themselves will be used subsequently by the scammer to continue to exercise leverage on the victim through blackmail.

The Psychology
Two major contributing factors that make romantic military scams so effective are the link between our perception of authority (people in uniform) and the sense of affinity and closeness we attribute to written communications. In a paper titled An Attributional Extension of the Hyperpersonal Model that analyzes the relationship between computer-based communications and the willingness to disclose intimate information, the authors conclude that:

“…In the experiment participants were randomly assigned to a face‐to‐face or computer‐mediated interaction with a confederate who made either high‐ or low‐intimacy self‐disclosures. Results indicated that computer‐mediated interactions intensified the association between disclosure and intimacy relative to face‐to‐face interactions, and this intensification effect was fully mediated by increased interpersonal (relationship) attributions observed in the computer‐mediated condition.”

The reason why we trust people in uniform is obvious but paradoxically, it seems that we are willing to disclose more intimate details about ourselves via electronic communications than in person. The written nature of these exchanges also adds a level of conviction and earnestness that verbal communications lack. In the case of romantic scams, this results in the victim developing a higher degree of confidence in what the other party says because they view the ‘physical’ text messages, emails, and letters in their possession as proof of sincerity.

The Scam Lifecycle
Regardless of the specific instance details, all scams are formulaic and follow a pre-defined lifecycle. Every successful romantic scam has these five sequential phases:

  1. Establish an online profile and make contact with a vulnerable victim
  2. Develop a trust relationship with the victim, by exploiting some of their weakness, isolate them, and groom them
  3. Learn as much as possible about the victim’s family, background, desires, and assets
  4. Gain access to the victim’s money or resources
  5. Use the victim as a money mole or contraband mover to support other scams

The Law Enforcement Challenge
Traditional crime or terror network analysis is expensive, time-consuming, and resource intensive. It requires dedicated task forces, access to fused HUMINT, GEOINT, MASINT, SIGINT, TECHINT, INT/DNINT, real-time surveillance data, and the support of many subject matter experts. Trying to reconstruct a modern criminal or terror network is not a straightforward matter because these networks do not follow the classical organized crime models and rigid hierarchical structures.

Cybercrime networks and operators are loosely coupled and are assembled from among a tightknit group of ethnically related individuals. They lack central organization, command hierarchy, official communications channels, and consistent geographical location. They are shielded by local politicians and have the full support and cooperation of senior law enforcement officials. These organizations also utilize a brutal code of loyalty and enforcement that can result in violent punishment for any member that deviates from their assigned responsibilities.

To complicate things further, most of the individuals involved in cybercrime networks are non-US nationals that operate overseas in multiple international jurisdictions. They also effectively utilize countermeasures such as data anonymization techniques, burner phones, and non-traceable payment methods to evade detection and tracking.

So the upshot is that traditional law enforcement and prosecution techniques like the use of informants, bank accounts or assets seizure, and search warrants are mostly ineffective and practically useless.

The Analysis
The ultimate goal of this research wasn’t to locate specific individuals but rather to discover the patterns of life, flora, fauna and lay of the land in this mysterious Kingdom of Scam-Land.  I personally have no interest in the individuals identified at all but I do believe that the information and analytics framework used here would be useful in educating the general public and Western LEA in trying to develop an automated and more effective real-time scam fighting strategies and programs. You can find more details about the solution architecture and system components that I’ve used in this project in a previous post titled the Mechanics of Deception.

The Analytics Framework
To generate the synthetic imagery, interactive dialogs, and wide-scale video and image searches, I’ve utilized the following software tools and machine learning frameworks:

  • Facial Imager – AI style-based image generator architecture
  • Rasa – Machine learning based conversation engine
  • IBM Watson – Text to Speech generator 
  • Audacity 2.3.1 – Open-source digital audio editor and recorder
  • Sentinel AI Platform – face recognition, facial Indexing, image reconstruction, and object detection

In the machine learning and detection components I’ve utilized: Convolutional Neural, Support Vector Machine, Multi-Task Network Cascade, CAFFE Framework, Haar Cascades, Ensemble of Regression Trees and Learning, P-N and Supervised Learning.

For the video analytics, I’ve trained the ML engine to detect known scammer characteristics such as certain car makes, fashion brands, accessories, and hand signs. The training of the model included the extraction of known objects (Image 3) as well as seeding the engine with new insight matches that were confirmed to be related to some relevant search topics. So, for example, the results from a search of a person wearing a Gucci belt buckle yields new insight that these individuals also carried certain pattered style bags. So moving forward, new searches incorporated the patterned bags into the query criteria, etc.

Object Search Criteria
Image 3: A sample of graphic objects used to train the ML-based search engine used to identify fashion and other accessories with known scammer associations.

The object of interest database contained about 6K classified samples. When fully operational, the system was delivering an object match true detection rate of about 86% accuracy. The total number of objects found and confirmed as relevant was over 17K (Image 4). The total runtime for all object detection and re-classification was about 9 hours.

Golden Watches
Image 4: A sampling of a results set for video and image searches using a combination of “dark skin shade” + “golden watch” objects.

Another type of object classification was used in combination with the BigData engine to locate certain keywords like “prepaid” and identify fashion trends like clothing styles. This included a combinations of more than 23K stop words and graphic object like shoes, shirts, pants, bags, and various accessories. The stop words were scraped from both OCR images, text, and speech to text conversion.

Distressed Jeans
Image 5: An example trend detection involving “distressed Jeans”.
   Scammer Shoes
Image 6: An example of a trend detection involving “visible toes” footwear.

Car Keys
Image 7: An example of a trend detection involving car keys suspended from the belt loops.

The Fashion of Yõüñg Blãck Amëricäñ
Image 8: An example of trend detection involving daily wardrobe arrangements and custom made designer outfits and shoes belonging to the scammer “Yõüñg Blãck Amëricäñ”.

The Fashion of Theophilus Adugyamfi
Image 9: An example of video cataloging and identification of a fashion trend of T. Adugyamfi aka “Billion$King”.

The Scammer Models
Image 10: An example of a trend detection of a “modeling” pose that is characterized by the subject facing the ground.

Hand Signals
Image 11: An example of a trend detection of over 40 types of “hand signal” gestures.

The system flagged the matches, anomalies, and trends and then converted them into insight that was used in more complex queries. This resulted in the identification of several hundred trends such as haircuts, hand signs, body poses, footwear, clothing styles, and color palettes. One interesting anomaly turned out to be a fashion ritual that was practiced by some of the scammers where they would once a week arrange their wardrobe in coordinated outfits (Image 8) for every day of the week and post them on internal chat boards.

A key component of the searching and linkage strategy was based on face recognition and face indexing. In total, I’ve indexed about 19K unique faces that constituted the POIs, their friends, families, and associates. The face recognition ran on multiple image sources such as CCTV cameras, phone cameras, laptop cameras, off-line video files, images stored in files, and social media albums and libraries.

Face Recognition KVNG DD
Image 12:
Usage of Face Recognition to identify an individual in a large collection of images and reconstruct their social and professional network. Featured in the image is “Play Bowy” and his “KVNG DD” team.

All image captures in real-time from CCTV cameras, phones, and laptops were indexed and processed for object and face matches and linkage analysis. The indexed images were then used for geospatial analysis to identify the place and time of individuals and group linkage.

Objects Database
The following is a summary of the objects in the inventory database:

Object Type



Facial Images


Used for Person of Interest (POI) searches

IP Addresses


Computer and other device IP addresses

Emails & Text Messages 38,145 Individual and group communications


Total number of individuals in the network



Make, model, and color

License Plates 156

License plate numbers

Image Patterns


Used for entity searches

Phone Numbers


Mobile and computer phone numbers

Financial Records


Account numbers and details

Voice Recordings


Phone and conference calls

Video Files


Video sessions

General Objects 17,209 General graphic objects like logos and graphics

Note on Speech to Text Conversions
Even though I captured several hours of conversation via open microphones and active chats sessions, I couldn’t accurately translate large parts of the content due to a limitation in Amazon’s, and Google’s transcribe and translation support for Nigerian and Gahanna dialects. In an operational deployment, this problem can be easily solved if a native language resource was available to review and translate the materials.

3 Way Conferance 
Image 13: Capturing a 3-way tech support Conference

Love is in the Air
It was time to get scammed. The first step was to create a fictitious person named Olga Schmatova and use her as bait.  I didn’t want to use stock photographs because of their easy tractability or utilize a real person for obvious reasons, so, I resorted to creating her images synthetically. I generated several photograph versions of Olga that accounted for age and weight variation. I then created the background information that supported her cover, such as a resume, biography, diplomas, online shopping profiles, employment history, as well as several other artifacts that would ensure that even an in-depth background search would yield valid results.

Olga-3 Olga-1 
Image 14: AI generated images of Olga Schmatova

Next, I created several social media, email, and dating site profiles and waited for the search engines to index her content. As soon as Olga started showing up in general internet searches, I was ready to go.

Olga Match
Image 15: One of Olga’s dating site profiles

Olga’s Dating Profile
My name is Olga. I am friendly, fun-loving, easy-going, and kind. My friends say that I bring a smile to the people around me. I consider myself to be balanced and a calm person at the same time I am very energetic, active and purposeful. I am full of positive energy and I project it to others.

I work as an emergency room nurse, I am family oriented and soft spoken, and at the same time I am a passionate and sensual woman, with oceans of love in my heart where we can swim together.

I like handmade art, visiting museums, cooking, and music is a big relief in my life.

Ideal Match Description
I want to find a man who is ready to accept my love and love me in return. I need stable and firm relations, I do not want to play with feelings or any drama. I would like to find a mature and wise man who is a doctor or a dentist who knows what he wants and can share with me some of his wisdom.

I didn’t have to wait long, within a several days I got the following hit:

Subject: Hello My 110% Match

Message: Wow was the first thing I said when I went through your pictures/ profile and I wonder why a woman like you is still single. Perhaps, all the men around you are blind (lol)…

We can hook up and hopefully, be wonderful soul mates… A little about myself. Hmmm, I am easy going and kind. When people meet me, they sense it before long. I am a military doctor with the US Army, I love epic movies cos I love adventure, stories from ancient history and anything related to real life.  I love the great outdoors, hiking, swimming, and travel.

I love family a lot! My favorite saying is “never look back”.  Well, the only thing lagging in my life now is that lovely woman that will follow me to my dream land. I believe ‘ONLY THOSE WHO SEEK, FIND’, and that is why I want to get to know you. I prefer direct email contact because of my deployment I don’t have access to this site at all times. Kindly email me on my gmail at the account at or leave your email address. Sometimes, we are dumbfounded answering love related matters cos love is beyond human comprehension. I Hope to hear from u soon. Cheers!


Just to make sure that this wasn’t a legitimate dating ping, I responded by asking Brandon for more personal details to which after several more emails and text exchanges he eventually sent me an image of his fake military ID (image 16). From the recycled verbiage in his responses (matches for which the search spider found online), the poor grammar, typos, and the fake ID, I knew that I found my scammer;

Subject: Dearest Olga,

Message:  I’m just a normal guy who is searching for real love and wants to enjoy life to the fullest, I’m an american soldier.I come from a family of 4 which is me, dad, mum and my sister.I have my own house in Florida.We were living happily until my wife was diagnose of Cancer, it was a bad experience for me and my family it shattered us, my wife leaved with it for good 6 yrs before she gave up. It was a terrible experience but i just have to move on and hoping for the best. I have a daughter who I love dearly. My favorite color is blue (sky blue), Sunday is one of the day i don’t like , I lost my wife on Sunday so i don’t feel so much happiness on that day or maybe you can help me through that.

Been in the US Army for 5 years now as a surgeon. I enjoy my job but on deployment life is hard because you are restricted to do some things but I’m used to it. I love my job and I really need a family at the moment because I think we all deserve happiness in this life, My personality is outgoing and I have a good sense of humor. I am a kind and very tender hearted person. I am loving, in fact I sometimes think that I love too deeply when I love someone. I am a very honest person. I am too trusting at times and I am loyal to my companion and friends. i have a romantic heart and I like that. I want to be respected for being a man. I like to act, dress and look like a guy. i’m looking for a nice, honest,kind and caring woman, a woman that will love and be with me for the rest of my life. I’m not looking for someone to date but someone to spend the rest of my life with. All i need from her is just being sincere to me,make me feel secured,appreciate,love,care and being understanding. My recent life has been engulfed with misery and loneliness.

Everything in life happen for a reason, my being lonely for a very long time makes me want a companion and a woman to share my feelings with, I’m in search of a soul mate to spend the rest of our life together. No one is perfect and we could only give it a trial. Though it is right for us to learn from our mistakes. Which makes us resisting and having hard time trusting again in your life. A relationship is all about TRUST, SINCERITY and HONESTY, all this Paramount fact must exist in a relationship before anything can work out of it. Faith is the substance of things not seen, but the evidence of things being hoped for, lets strengthen our faith and see where this will lead us to. You are a beautiful woman and I want to get closer to you and see maybe this will work out for us.

Yours truly,


Fake Militery ID
Image 16: Brandon’s fake military ID with scraped internet image of the Russian actor Oskar Ruchera.

Our romance lasted for about two weeks and included several exchanges of emails, voice, and text messages. I am not going to bother you with all of the details of the lengthy and steamy conversations that ensued. The skinny of it is that Brandon loved me and wanted to spend the rest of his life with me. We were going to get married in Indonesia and spend the honeymoon in Nusa Penida! All of this, of course, depended on him completing his “contract” combat deployment in Afghanistan in two years, where he served as a doctor.

Then came the con. Brandon said he could leave earlier and meet me in Indonesia, but he would have to pay the US Army an administration fee of about $20,000, which he didn’t have because all of his money was tied up in some high tech investment in Google. He promised that if I helped him with this payment, he would repay me the money with interest in no time. I then received an email from who claimed to be one Colonel William Daniels of the US Army HR team requesting that I transfer these administrative fees to him via Western Union.

I didn’t have $20K on hand, so instead, I decided to give in to Brandon’s insistent requests for some intimate pictures of me. Following Ursula’s advice to the Little Mermaid that one should “…never underestimate the power of body language!” I sent Brandon several glamor shots of Olga. I also added some extra loving payload to the images to help take our relationship to the next level.

Olga Swiming Olga in a Little Black Dress Olga Glamour Shots
Image 17: Olga in a swimming suit and in and out of her little black dress.

Olga’s AI generated voice

The payload deployed successfully and the initial probe came back indicating that most machines on Brandon’s sizable local network were running Windows 7 and were unpatched. That was followed by several more payloads. I mapped his network and devices and started monitoring his internet traffic and messaging activity. Interestingly, Brandon and his team were somewhat security conscious as they were using dedicated machines for scamming and separate computers for banking and business operations and logistics. They were also careful to use burner phones for business communications and other phones for voice and internet browsing.

While taking inventory of the drives and storage media, I found a directory full of images that Brandon was using to assemble his social media profiles on what appeared to be a graphics editing machine. surprisingly, it even had a licensed version of Adobe Creative Suite. As the VA spider was sifting through these images, it flagged two anomalies. The first was the OCR’d word “Verizon LTE” (Verizon doesn’t operate in Ghana), the second was a face match on two mobile phone screenshots of a FB page that contained the fake photos of Brandon and actual photos of two scammers (Image 18).

Burner Phones
Image 18: Phone screenshots capture used for the network entry point.

Now that I had actual faces, the FR took over and traversed the photograph datasets on his storage devices and on his and his network’s on-line profiles and filled-in the matching names, addresses, phone numbers, car license plate, make, and car color (image 19). Altogether, the data collection took about 24 hours and involved some man-in-the-loop operations like the verification of anomalies and the termination of dead-end searches.

One interesting insight from the face recognition mapping is that Brandon and other individuals on his network use multiple ‘legal’ names and alternate spellings in official government documents such as drivers licenses and passports. Brandon, for example, was using the names “Nana Osei Kwadwo Boakye”, “Osei Kwadwo Boakye”, and “Osei Kwadwo”.

LPR Make and Color
Image 19: Sample from over 150 license plates, car makes, and color details collected from the scammer network.

From the keystroke logs and login schedules, it was clear that multiple operators were involved as each exhibited distinctive ‘fist’ through Keystroke dynamics. Monitoring the login activity in real-time and screenshots confirmed that many of the operators shared the login details for their ‘bait’ profile.

The Scammer Enterprise and R&R
One surprising find was that the scammer operations used specialists. This makes sense because If one scammer had to carry out all of the work individually, it would take him years to develop the necessary cross-domain expertise. The substantial resource expenditure required to obtain the logistic and operational proficiency needed to support a massive scam campaign such as this would also be time consuming and intellectually prohibitive. In the scammer community, members specialize according to their interests and talents; this allows them to more rapidly reach higher levels of productivity.

The person’s roles and responsibilities in the layered scammer enterprise depend on their ranking within the organizational structure. At the lowest level, there are the ‘factory workers’ who conduct their operations in teams of 5-15 individuals in internet cafes located in the suburbs and villages surrounding Lagos and Accra. Most don’t own their equipment and pay a fee for computing resources and internet access. At the next level are the home-based operator ‘managers’ who own their equipment and manage several low-level teams. These managers are responsible for training, recruitment, mentoring, and operational support for the lower tiers.

The next level above them are the ‘supervisors’ who are responsible for maintaining proper ‘sales’ quotas, conversation rate, and logistics and the development of new scam products.

At the top of this pyramid are the kingpins who control several territories. They liaise the with senior command of law enforcement, military, media, and politicians and are considered to be the pillars of the community. They engage in wide-scale philanthropic activity such as sponsoring public works and charity.

Dr. Osei Kwame Despite Wiamoase Police Station in Ashanti Region Wiamoase Police Station donated by Osei Kwame
Image 20: The opening of the new Wiamoase Police station in the Ashanti Region. Courtesy donation by Dr. Osei Kwame.

An interesting anomaly that was later confirmed to be a wide scale trend was the frequent appearance of President Obama’s picture in many of the scammer workspace images (Image 21). Seventeen out of forty workspace images exhibited this trend. It turns out that BHO is considered to be the patron saint of the scammer industry in Nigeria and Ghana and is viewed as the ultimate scamming success story that everyone is trying to emulate.

The Office
Image 21: (L-R) Tier 3 scammers “KingKash” hard at work in the office and “Baron Osty” in a live session with a mark. Many scammers keep images of President Obama in their workspace and view him as the patron saint of the craft of the scam.

The Anatomy of a Scam
Based on the large volume of case data retrieved from the computers on Brandon’s network and the postmortem on these cases, it appears that many operators run hundreds of concurrent scams. In one example from January 2017- February 2018, a mid-level manager/supervisor named Asuaden Rich AKA “Goal Digger” (Image 22) operated over 105 simultaneous scams in the US alone with 40 of these scams paying over $630K USD. For these, “Goal Digger” assembled and used a library of over 300 stolen identities from Facebook and LinkedIn.

Between June 2018 and August 2018, Goal Digger spent on average 7 hours a day working 7 days a week, splitting his time between boarding new victims and maintaining active accounts.

The Dating Setup
Image 22: An illustration of the scam details run by a the tier 2 scammer “Goal Digger”.

Money Makes the Scam go Around
Since most victims are too embarrassed to report their ordeals, it is difficult to calculate the exact amount of money lost each year as a result of these scams. In 2013 the estimated figure was about $12.7 billion dollars. Adjusting for a modest 10% growth per year could bring it to 20 billion dollars. So, this industry makes more than the GDP of counties like Albania, Jamaica, and Afghanistan.

Just like in any other form of illegally generated revenue, the vast amounts of money that is collected from these scams must be laundered before the money can be used. In the case of the Nigerian and Ghanan networks, most of the placement, layering, and integration activity associated with money laundering took place locally via cash purchases of luxury goods, investment in large commercial and residential real estate developments, and the purchase of drugs, arms, and human trafficking. Additionally, in at least several instances, the scams were also financing Jihadi operations in Africa and overseas. 

The Package
Image 23: An example of a UK based courier services used by Brandon’s network for mailing goods and money laundering. The UK network is also affiliated with a local Stockton Jihadi mosque and recruitment center and a number of active Jihadist.

It is worthy to note that local law enforcement and financial institutions are full participants in these ventures. The scammer networks also enjoy the full support of the local media. For example, the banks and money transfer/exchange organizations that service the scammers deliberately misplace the security cameras at the branches in order to avoid capturing the faces of the scammers. They also frequently purge their VMS systems and any internal records that contain the scammer’s national identification numbers, address, or other traceable PII data.

Richard Mark Hooten AKA Nana Kwadwo Boakye EcoBank Cash Pickup
Image 24: Nana Kwadwo picking up the cash transferred via Western Union at the Ecobank branch near Medina Estate Gbagada Lagos. Camera field of view inside the teller does not capture the customer’s face.  Cameras mounted on the branch ceiling only provide a rough view of customers.

Richard Mark Hooten AKA Nana Osei Kwadwo Shows me the money The 500K Run Weekly Payment
Image 25: (L-R) Daily cash withdrawals, over $500K in freshly laundered money and weekly police and political payment bundles.

Deep Gray the Transporter
Image 26: Over a million dollars transported to Nigeria from Ghana via charted flight by a tier 1 scammer.

Scammers International
All of the Nigerian and Gahannan scammers networks examined show that they also maintain an international presence in many western countries like the US and Europe. These relationships are based on family members who either legally reside in the host country or arrive there on tourist visas. These relationships provide a home-based network with access to local organized crime for the purpose of local money laundering and goods transfer. For example, Brandon’s network included individuals in multiple cities in the US, England, Germany, Italy, Bulgaria, and France.

US Network
Image 27: One US node of the network.

Brandon’s network was also working closely with several local UN officials and aid organizations who either had their own or utilized UN and NGO trucks, planes, helicopters, and facilities to transport and store cash and drugs in and out of the country.

The International face of scamming
Image 28: Osei Kwadwo Boakye AKA Brandon Smith and his domestic and international scammer network.

The Derivative Economy
An interesting aspect of the scammer economy is its deep integration into the local marketplace.  Many of the ‘manager’ and ‘supervisor’ level scammers eventually branch out and establish a variety of legitimate businesses such as appliance and computer stores. The seed money and inventory for these ventures initially comes from the scams, but eventually as these ventures mature and develop a customer base and revenue traction they switch and operate as legitimate businesses. In one illustrative example, a scammer named “Ruona Fundz” built a small retail empire of 6 electronic stores (Image 28), all of which were managed and operated by his extended family.

The Retail Empire of Ruona Fundz
Image 29: Reselling the loot, the electronics retail empire of “Ruona Fundz”.

Network Structure and Communications
All cybercrime networks depend on the contributions of a wide cast of professionals working in concert including malware developers, scriptwriters, customer-facing operators, spammers, botnet masters, payment processors, IT, InfoSec, and, finance. These communities require efficient and reliable methods to collaborate, communicate, and coordinate by sharing tips and tricks that help them defeat security measures and evade detection.

The stratified division of labor in the scammer networks allows actors to specialize in domains for which they have natural talent. This facilitates ‘personal growth’ and the achievement of the level of expertise in their particular area of specialization beyond what could be accomplished if they were responsible for all of the elements in the scam chain. Also, the acquisition of human talent is simplified because the professional barrier of entry is lower for newcomers. As the recruits don’t have to spend significant time and money, building the capacity themselves and just have to purchase a starter kit and be productive immediately.

The traditional meeting place for cybercriminals used to be online messaging boards or web forums. There, scammers would get together, obtain support, buy technical tools like phishing kits and malware, and sell their illicit gains. Due to the effective shutdown of many of these sites by western law enforcement, this is no longer the case. This resulted in the wholesale migration of the scammer community to various peer-to-peer (P2P) messaging platforms.

The majority of the scammers on Brandon’s’ network utilized one or more of the following tools for their business communions:

  • Skype
  • Telegram
  • WhatsApp
  • Burner Phone messaging
  • Social Media messaging

Deep Grey Dead Grandma Messaging
Image 30: Bogus postings of an obituary used for email, user ID, and password exchanges.

The Scammer InfoSec
Brandon’s network had a playbook for information security that included pointers on how to secure voice and data communicators (via encrypted channels like Skype and WhatsApp) and even advice on maintaining anonymity on social media. For example, some recommendations included best practices, such as:

  • Using burner phones for all business exchanges. The burner phones are typically recycled every 3 months
  • Obfuscating their car license plate numbers in images that they share on social media
  • Always using aliases and never use their actual name in any online communication
  • Obscuring the house number and street name in images uploaded to social media
  • Recycle work computers once a year
  • Not traveling internationally especially in the US with any ‘work’ equipment
  • Disabling Geo-Tagging and GPS tracking on cellphones

Interestingly, most individuals in the network opted not to follow many of the InfoSec safeguards and precautions. In several cases, Ghana-based laptops showed up in Europe and the US. The exit nodes of these were not TOR based, and they used local hotel and residential ISPs. In two examples, laptops that belonged to “Prince Pero” and “Halid The-General” that were used for dating site scamming activity in Ghana, beaconed an IP address in Marietta, GA, and Chicago, IL. From IP address history and use, it appears that their owners came to the US, traveled there for 2-4 weeks, then headed back to Ghana, all the while continuing to scam their victims.

Burner Phones
Image 31: Extensive use of burner phones for internal communications and coordination.

car license plate obfuscation by scammers
Image 32: An example of a car license plate obfuscation by scammers in order to prevent traceability on images posted to social media.

Summary of Findings
When I started the search, I only expected to identify a handful of enterprising individuals that ran these types of scams from an internet cafe or their basement. But I was wrong. I was taken aback by the sheer scale and sophistication of these operations and the amount of revenue they generate. It also became clear that these scams are not private operations that are run by some enterprising individuals but rather a national industry that involves banking, law enforcement, the media, some UN bodies (mostly local), politicians, and local and international corporations across multiple jurisdictions.

Tracing the 1-degree operator network in the city of Madina in Ghana alone yielded over 10K individuals (total city population is about 132K) that were directly active in scamming. This represents about 7.5% of the total city population. A 2-degree linkage would triple this figure. If we account for a similar distribution across the whole of Ghana, we could potentially be looking at 15%-30% of the entire population working in or supporting scamming as a national industry. And these numbers don’t account for the impact that hundreds of millions of dollars have on the overall Ghana’s economy and political structure. It is revealing that the official 2016 -Statistical-Report-Ministry-Of-Employment-and-Labor Ghana report completely ignores this phenomenon and fails to account for this market segment/industry as a major source of national revenue.

Most of the scammers identified in the analysis fit into a specific age and socio-economic cross sections. They are between 14-30 years old, and the majority have some high school education. Many had IT training through a local network of centers called IPMC. Only a handful have college degrees, and these are mostly supervisors. The majority of the member of the network come from the surrounding villages and towns around Accra, Abuja, and Lagos.

Scamming is a family business and is based on personal relationships; this is especially the case for the low-level operators. Many of them arrive to the city in search of fame and fortune from smaller villages and live in crowded rentals with as many as 5-10 per room.

Baron Norway and the Rich Bad Boys
Image 33: “Baron Norway” and his Tier-3 “Rich Boys” team chillin’ out.

The operations have a quasi-hierarchical and fluid structure that is based on tiers. These tiers are roughly divided to the low-level teams (tier-4-5) that work in the internet cafés (which are owned by the tier-1-2 operators). They are responsible for creating the on-line profiles and generating the leads and the constant cash flow. The tier-2-3 have management responsibilities and run multiple production facilities that can range from 10-50 tier 4-5 operators. They are responsible for recruitment, training, and meeting sales quotas. The tier-1 group sits at the top of the pyramid and managers multiple tier-2-3 managers. They finance the overall operations, provide the political and security coverage, and also collect the bulk of the revenue from their territories.

The Tier System
Image 34: The scammer tier hierarchy.

This tiered structure is, to a large degree, merit based and provides for constant upward mobility. In many ways it resembles a classic sales organization: the more you sell, the higher your commission is and the faster you move up to a more lucrative tier. It is not unusual to see a tier-5 operator move to tier-2 level in just a few years. 

From rags to riches
Image 35: From rags to riches in 4 easy steps. The Cinderella success story of “Goal Digger”

The tier-1 operators only function in specific territories that are controlled by the police and politicians. They have strict “payment” responsibilities for their territory.

Fast and furious
Image 36: Examples of some of the sweet rides owned by the tier-1 and 2 scammers. The cars are exclusively Mercedes, Lexus, Land Rover, or Porsche.

The revenue collection is controlled by a strict commission structure with every individual who is involved in the supply chain taking his cut based on the following rates per transaction. 


Commission Per Transaction in Percent


~ 5

Military (if involved)

~ 7


~ 10

Political Patronage

~ 10

Tier 5


Tier 4


Tier 3


Tier 2


Tier 1


The police provides the security services to protect the carriers and money as it’s transferred to various locations in the city. The police will also provide the scammers with additional for fee ‘anti-robbery’ protection to ensure that nonaffiliated gangs won’t whack them en route from and to the bank. 

One illustrative case for this protective service (Image 36) is the altercation between the police officer Frederick Amanor Godzi and Ms. Patience Osafo at Midland bank branch in Ghana. The popular explanation for the beating she received was that she was at the bank trying to withdraw about $50 USD to buy some food for her baby grandchild and that the bank refused to give her the money. This lead to an argument with the bank manager who called the police. The policeman then proceeded to beat her up and literally kick her out of the bank. But the internal communications suggest that she was working as a lookout for an armed gang that was waiting nearby for scammers to come into the bank to withdraw large amounts of cash.

Police at the bank Pay Day
Image 37: Police beating at the Midland bank and “Crym Payys” and “Da General’s” doing the weekly cash runs.

After the initial public outcry that followed the posting of the violent video on social media, the court and the police ended up dismissing all charges against the policeman and the bank employees. The reason given was that the victim failed to appear in court to testify and cooperate with the police investigation. Ms. Osafo ended up getting a new luxury house as compensation and the bank officials even released the following statement:

“We just closed a generous offer that removes her from the ‘kiosk’ to owning a brand new house, and from the streets hawking toffees to now owning multiple bank accounts“

Ironically, Ms. Osafo’s house as well as several other developments in the Fettah Kakabra Top King estate were funded by scammer investment money.

This form of collusion between politicians, justice officials, law enforcement, and the financial institution where they ‘work things out privately’ seems to be the norm in scammer land and at least partially explains why these environments are so fertile in supporting the massive growth of the scamming industry in these countries.

Police at the Bank Beating
Image 38: The main characters of the Midland Bank police beating saga.

Law Enforcement and Scamming
The police strictly enforces ‘permits’ to operate and the payment of commissions from the scammers by partnering with the money exchanges and financial institutions. For example, the security guards employed by the electronic payment vendors are also responsible for collecting a copy of each money transfer invoice that is cashed to ensure proper accounting of all payments. As can be seen from Image 39, the failure to pay or any attempts of cheating is dealt with promptly and severely.

Protection Money
Image 39: Money collection and the payment of ‘protection money’.

The payment to the scammers varies based on KPI such as closing rate, month to month performance, and seniority. The banks, police, and other facilitators such as the UN/NGOs, and politicians typically deduct their commissions at the teller. What is left is then distributed among the hierarchy on a weekly basis. The final form of payment can take different forms including cash (in local or foreign currency), drugs, or even gold bars.

Payment Methods
Image 40: Forms of payment include pre-paid bank cards (Access Bank), gold bars, cash, pre-paid cards, drugs, electronic funds transfer, pre-paid ecommerce cards, laptops, and phones.

Real estate development
Image 41: Example of large scale money laundering operations through investment in residential development projects in Ghana. The Qyarifa development, one of dozen such projects cleared over $12 million USD through the sale of units each going for $65K-$100K USD.

International Travel
Image 42: International travel and coordination with local scammer teams. Global scammer networks are located in many major cities in the US, Europe, and Asia

Show me the money
Image 43: “Crym Payys” at home piling up the cash and bling.

There is no silver bullet solution for keeping vulnerable individuals safe online but there is a lot that we can do to strengthen our ability to disrupt and destroy the scammer networks. International cooperation is important in tackling this ever-growing form of cybercrime but ultimately it is important to remember that some UN personnel, diplomats, government policymakers, NGO’s, politicians, media, and local law enforcement officials in the host countries are the largest financial beneficiaries of this industry and are themselves part of the problem.

As the data suggest, the solution to the scammer challenge is not to use more law enforcement to post-fact persecute the perpetrators but rather to preemptively disrupt and destroy these networks before they become operational or drain them financially once they are active. This is a classic OODA loop problem that every large western intelligence organization deals with every day. Even the scope of gathering, processing, and analyzing national security information from around the world fits in squarely with the capabilities and charter of agencies like the CIA and NSA which already collect this type of intelligence and operate on it in similar environments internationally.

The public attitudes toward cyber scammers influences the way that government, law enforcement and commercial companies regard cybersecurity in general. If they think that the public does not perceive cyber scamming to be a serious threat to their personal wellbeing, then they won’t place a high priority on regulation, enforcement, and platform security.

Although history doesn’t repeat the same exact tune, it does tend to play the same scales over and over again. In 75 BCE, the 25-year-old Julius Caesar was sailing the Aegean Sea when he was kidnapped by Cilician pirates. According to Plutarch, the pirates asked for a hefty ransom for his release and Caesar sent several of his friends to Rome to gather the silver, a task that took 38 days. Caesar told the pirates that after he was set free, he would hunt them down and hang them. Once he was freed, he made good on that promise: he quickly raised a fleet in Miletus and went right back to Pergamon where he had been held captive. He captured the pirates and as he promised ordered that they be crucified.

The moral of the story is that cybercrime is the modern version of piracy and the only effective way to deal with it is with diligence and force. The pirate infestation of the cost of Somalia is a good illustration of what happens when these type of problems are not dealt with promptly. The current approach of trying to combat international on-line fraud by relying on international treaties, the Interpol, and UN resolutions amounts to little more than standing in a malaria-infested swamp and swatting at individual mosquitos.

You can rest assured that there is no need for Caesar’s brutal form of punishment to solve this problem. Following these six easy steps is guaranteed to quickly drain the international scammer swamp:

  1. Place OFAC sanctions against key cybercrime, political, UN, NGO, and law enforcement personal involved in the scamming activity.
  2. Shutdown the access of the cybercrime facilitating states to internet services like SWIFT, Airline Reservations System, etc. for several days/weeks as a warning.
  3. Start an international media campaign to inform the scammer sponsoring governments that their continues support for this industry would have severe consequences to their economy.
  4. Create and maintain a real-time database (using a framework such as demonstrated here) of all known scammers and arrest them when they attempt to travel internationally. The required bond for their release should be the total amount they scammed plus interest. That money should then be electronically transferred back to the victim’s accounts.
  5. Charge the management of the dating sites and social media hosters that so far have done little to combat this phenomenon with negligence and open them to civil actions by their customer victims.
  6. Bring RICO, mail and wire fraud, and money laundering charges against the C-level executives/board members of the US-based money transfer companies that are the lifeblood of these scams.

Finally, If you are reading this Captain Brandon, it was fun while it lasted, but I don’t think that this relationship is going to work out, it just wasn’t meant to be chéri. 


I'm not that kind of a girl

The Disclosure–Intimacy Link in Computer Communication – Human Communication Research
Understanding Romance Fraud: Insights From Domestic Violence Research – The British Journal of Criminology, Volume 58, Issue 6, November 2018, Pages 1303–1322.
Generating and Tuning Realistic Artificial Faces – ML based face generation by Rani Horev
A Style-Based Generator Architecture for Generative Adversarial Networks – Tero Karras NVIDIA Research
A Style-Based Generator Architecture for Generative Adversarial Networks – Cornell University
Generative Adversarial Networks  – Cornell University

Articles and Sources About Online Romance Scams:
Better Call Harry: Stolen Heart, Stolen Identity
This Army Veteran Became The Face Of Military Romance Scams. Now He’s Fighting Back
Brown County Browser: Don’t fall for veterans romance scams
Fake US Soldiers Robbing Women Online
How a billion-dollar Internet scam is breaking hearts and bank accounts
“Prince Charming” Behind Bars: Nigerian Romance Scammer Nets 27-Year Prison
Love a man in uniform? Online dating scammers hope so
Love me don’t: the West African online scam using U.S. Soldiers
Australian grandmother on drug ice charges in Malaysia: Maria Elvira Pinto Exposto may be victim of a military romance scam



Copyright 2019 Yaacov Apelbaum, All Rights Reserved.