Determining, measuring and testing quantitative signatures of deceptive behaviour

Given the frequent and often successful attempts at trafficking illegal items of high value across borders, many systems have been put in place (e.g. airport baggage screening) to detect such attempts. However, given the limitations of these current systems, this study investigated the prevalence of visible behavioural signatures to concealment that could be seen by a multiple camera set-up. Ten participants were asked to conceal a high value item of which they could keep if they could successfully lie to our “lie detector machine”. 13 cameras observing every visible area of each participant were used to collect over 500 videos for analysis of bodily movement. Each participant underwent a conceal condition and a separate baseline condition where they did not conceal any items. 1500+ quantitative measures of bodily movement, including response time, were performed. It was found that, in the conceal condition, response time to the critical question increased, hand movements decreased, blink rate increased, and the left foot was nearly always in front of the right. In conclusion it appears that, within our experiment, there do exist behavioural signatures for concealment that could be used in automated screening applications. Further work to address the limitations of this study including ecological validity will follow

Can lies be faked? Comparing low-stakes and high-stakes deception video datasets from a Machine Learning perspective

Despite the great impact of lies in human societies and a meager 54% human accuracy for Deception Detection (DD), Machine Learning systems that perform automated DD are still not viable for proper application in real-life settings due to data scarcity. Few publicly available DD datasets exist and the creation of new datasets is hindered by the conceptual distinction between low-stakes and high-stakes lies. Theoretically, the two kinds of lies are so distinct that a dataset of one kind could not be used for applications for the other kind. Even though it is easier to acquire data on low-stakes deception since it can be simulated (faked) in controlled settings, these lies do not hold the same significance or depth as genuine high-stakes lies, which are much harder to obtain and hold the practical interest of automated DD systems. To investigate whether this distinction holds true from a practical perspective, we design several experiments comparing a high-stakes DD dataset and a low-stakes DD dataset evaluating their results on a Deep Learning classifier working exclusively from video data. In our experiments, a network trained in low-stakes lies had better accuracy classifying high-stakes deception than low-stakes, although using low-stakes lies as an augmentation strategy for the high-stakes dataset decreased its accuracy.Comment: 11 pages, 3 figure

Effects of deceptive behavior on biomechanical measures of standing posture

Title from PDF of title page, viewed on June 22, 2012Thesis advisor: Gregory W. KingVitaIncludes bibliographic references (p. 82-88)Thesis (M.S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2012The accurate detection of deception has potential applications in many fields including credibility assessment, security screening, homeland security, and counter-terrorism. Techniques currently used for deception detection typically capitalize on deception-related physiological changes, and include polygraph testing, voice stress analysis, brain activity analysis, and thermal scanners. However, the use of these techniques in natural environments is limited as they often require intrusive sensors to be attached to the body. These limitations may be addressed with posturography, which involves studying the ground reactions associated with standing balance without the need for intrusive sensors. Therefore, the objective of the current study was to examine deception-related effects on measures of standing posture using a mock security screening interview. We hypothesized that deceptive participants, compared to truthful would demonstrate significant differences in ground reactions during the interview. Participants were required to pack a backpack with various items. One group of participants had items that were "prohibited", whereas the other group had equivalent, non-prohibited control items. Both groups were questioned about the contents of the backpack. The group with "prohibited" items was instructed not to reveal that they were carrying any prohibited items. Results of the study indicated that there was a significant deception-related decrease in center of pressure movement. The deception related decrease in both center of pressure pathlength and mean velocity suggests that people "freeze" when they are being deceptive. This notion was supported by increased oscillations in the anterior-posterior direction.Introduction -- Methods -- Results -- Discussion -- Conclusions -- Appendix A -- Appendix