Lie Detection

1. Lie Detection by Vinod Reddy (09005071) BhanuPrakash (09005050) Hasan Kumar (09005065)

2. Outline • Introduction • Human Lie Detection • Techniques • Micro-Expression based design • Controversy & false-positive results

3. Introduction • Lie detection is the practice of attempting to determine whether someone is lying. • Usually this involves asking the subject control questions where the answers are known to the examiner and comparing them to questions where the answers are not known. 

4. Why is deception detection important? • Lie Detectors(though not an accurate name) • In event of crime, • Can be used in interrogating • To find the truthfulness of the evidence.

5. Why is deception detection important? May prove useful when • hiring potential employees • employee theft • revealing whether or not your future spouse/girlfriend truly loves you or is after your money • Dealing with your stock broker, sales rep, lawyer, ex-wife, car dealer, mechanic, scam artist, etc.

6. Lie Detection Techniques • To detect whether a person is lying, it is important to know what to look for in the person which shows he is lying. • For this we need to know when a lie fails.

7. WHEN DOES A LIE FAIL ? • Two reasons. • Failed to adequately prepare a lie. - lack of adequate thinking • Interference of emotions - lack of control on emotions

8. WHEN DOES A LIE FAIL ? • Two reasons. • Failed to adequately prepare a lie. - lack of adequate thinking • Interference of emotions - lack of control on emotions

9. Inadequate Preperation • Lies often fail because of inadequate preparation • When liar comes up with a lie at the spot • May contradict himself • Being caught off gaurd when asked questions which the liar didn’t anticipate.

10. Interference Of Emotions • Lies also betrayed by signs of emotions • Simplest case is when the liar fabricate convincingly an emotion which is not felt. • Involves concealing his own emotion. • Two types of failures • 1)some sign of emotion is revealed • 2)the liar may produce some inadvertently • a deception cuewhich shows person is lying.

11. Lie Detection Techniques ( human ) • How do we usually guess whether the other person is saying the truth? • Based on the behavior of the person • Eye Patterns • Cadence of Speech • Body Language of a Liar • Emotional Gestures

12. Human - conclusion • Human lie detection capabilities are limited. • For example, a meta-analysis of 253 studies of people distinguishing truths from lies revealed overall accuracy was just 53 percent- not much better than flipping a coin.

13. Simple Lie Detector Build one home easily

14. Polygraph • A polygraph is an instrument that simultaneously records changes in physiological processes such as heartbeat, blood pressure, respiration and electrical resistance (galvanic skin response or GSR) • The polygraph was invented in 1921 by John Augustus Larson, a medical student at the University of California at Berkeley and a police officer of the Berkeley Police Department in Berkeley, California • The underlying theory of the polygraph is that when people lie they also get measurably nervous about lying. The heartbeat increases, blood pressure goes up, breathing rhythms change, perspiration increases, etc. 

16. Polygraph •  A baseline for these physiological characteristics is established by asking the subject questions whose answers the investigator knows. Deviation from the baseline for truthfulness is taken as sign of lying.

17. Polygraph • There are three basic approaches to the polygraph test :- • The Control Question Test (CQT): compares physiological response to relevant questions about the crime with the response to questions relating to possible prior misdeeds • The Directed Lie Test (DLT): detect lying by comparing physiological responses when the subject is told to deliberately lie to responses when they tell the truth • The Guilty Knowledge Test (GKT): compares physiological responses to multiple-choice type questions about the crime, one choice of which contains information only the crime investigators and the criminal would know about

18. Polygraph • Validity : Polygraphy has little credibility among scientists. A 1997 survey of 421 psychologists estimated the test's average accuracy at about 61%, a little better than chance. Critics also argue that even given high estimates of the polygraph's accuracy a significant number of subjects (e.g. 10% given a 90% accuracy) will appear to be lying, and would unfairly suffer the consequences of "failing" the polygraph 

19. fMRI • Functional magnetic resonance imaging or functional MRI (fMRI) is an MRI procedure that measures brain activity by detecting associated changes in blood flow • Studies using fMRI have shown that it has potential to be used as a method of lie detection. While a polygraph detects changes in activity in the peripheral nervous system, fMRI has the potential to catch the lie at the ‘source’.

20. fMRI • The procedure is similar to MRI but uses the change in magnetization between oxygen-rich and oxygen-poor blood as its basic measure • This measure is frequently corrupted by noise from various sources and hence statistical procedures are used to extract the underlying signal

21. fMRI • The resulting brain activation can be presented graphically by color-coding the strength of activation across the brain or the specific region studied • Using this method, studies have shown that lies can be distinguished 78% of the time

22. Brain Observations • Electroencephalography (EEG) is the recording of electrical activity along the scalp. EEG measures voltage fluctuations resulting from ionic current flows within the neurons of the brain • Brain fingerprinting uses EEG to determine if an image is familiar to the subject. This could detect deception indirectly but is not a technique for lie detecting

23. Brain Observations • Cognitive chronometry, or the measurement of the time taken to perform mental operations, can be used to distinguish lying from truth-telling • Brain-reading uses fMRI and the multiple voxels activated in the brain evoked by a stimulus to determine what the brain has detected

24. Drugs • Truth drugs such as sodium thiopental and marijuana (historically speaking) are used for the purposes of obtaining accurate information from an unwilling subject •  Information obtained by publicly disclosed truth drugs has been shown to be highly unreliable, with subjects apparently freely mixing fact and fantasy

25. Non-Verbal Behaviour • Non-invasive lie detection using non-verbal behaviour is performed by the Silent Talker Lie Detector • It observes and analyses non-verbal behaviour in the form of micro-gestures while a subject is being interviewed • It is grounded in the psychological theory that non-verbal behaviour is modified by a number of influences when a person is being deceptive. These include arousal (in particular stress), cognitive load, duping delight, and behaviour control

26. Linguistic Inquiry and Word Count (LIWC) • At the University of Texas at Austin, psychology professor James Pennebaker, PhD, and his associates have developed computer software, known as Linguistic Inquiry and Word Count (LIWC), that analyzes written content and can, with some accuracy, predict whether someone is lying. Pennebaker says deception appears to carry three primary written markers: • Fewer first-person pronouns. Liars avoid statements of ownership, distance themselves from their stories and avoid taking responsibility for their behavior, he says. • More negative emotion words, such as hate, worthless and sad. Liars, notes Pennebaker, are generally more anxious and sometimes feel guilty. • Fewer exclusionary words, such as except, but or nor--words that indicate that writers distinguish what they did from what they did not do. Liars seem to have a problem with this complexity, and it shows in their writing.

27. Lie Detector Design with fuzzy neural networks(FNN) • Need for it ? • Standard polygraph – easily faked • Not counted as evidence in courts • Differences from standard polygraph • Micro-gestures • Automated • No physical contact needed • No trained psycho-physiologist required

29. Building Blocks • An Artificial Neural Network (ANN) • Camera

30. System Flow Graph

31. WorkingPretest • Feed data to the system • Case to be investigated • Date and time of the crime • Details of the crime • Subject’s social background, medical history and criminal record (with cooperation of subject)

32. WorkingPretest Input Variable • Data is now scanned and checks for certain vindictive words • e.g. robbed, murder, jail • pattern matching techniques • Higher total number of incriminating words found, closer the value of I1 to 1. • I1 = fraction of such words found to total such words recognized by system.

33. WorkingGeneral test • Also known as relevant-irrelevant test. • Relevant questions – real issue of concern to investigation e.g. asking who did it, about evidence, etc. • Irrelevant questions – provoke no emotion • Irrelevant questions are typically asked first. • Physiological response of no diagnostic value • Guilty • Stronger reaction to relevant questions • Innocent • React similarly

34. WorkingGeneral test Input variables • Convert receiving operator characteristic (ROC) curve/graph (analog signal) to digital signal. • Difference of consecutive peaks and lows is taken and averaged out over total number of such differences to give I1i i.e., input variable for the ith response for the general test.

35. WorkingControl Test • Comparison question test • Ask about general undesirable acts. • Peak-of-tension test • Questions are asked in an easily recognized order. • A guilty examinee • Responsiveness increases as correct alternative approaches in question sequence • Decreases when it has passed • Others • e.g. probable-lie and directed-lie comparison tests, known-solution peak-of-tension test

36. WorkingControl Test Input Variable • Convert receiving operator characteristic (ROC) curve/graph to digital signal. • Difference of consec. peaks and lows is averaged out to give I2i i.e., input variable for the ith response in the control test • If there are n physiological parameters, • Then #input Variables = (2n+1) ( n – general test, n – control test and 1 –pretest) • Input variables are fed into neural network (trained beforehand) to generate output.

37. Generating Membership Function • Fuzzy vs crisp neural network • Membership functions vs weights • Obtaining the data set • (membership functions of each of the input variables) • # data regions = # cases used for training

38. Training • Similar to Feed Forward Crisp Neural Network. • Sigmoid neuron

39. Output • # output in neural network = n+1 • Today’s lie detectors, responses in the form of graph. • Fuzzy mathematical expressions must be brought to deal with such situations. • (n+1) membership functions combine to give a unique membership function outside neural network, which in turn must be defuzzified to give final output • Min of all membership functions – benefit of doubt to the examinee

40. De-fuzzifying final output • Mathematically, de-fuzzification of a fuzzy set is a process of rounding it off from its location in the unit hypercube to the nearest vertex. • Put simply For our system, we propose the value of λ = 0.5; i.e. for any output greater than 0.5 the output would be 1, otherwise 0.  In this case, the case 1 would mean the person is a liar, while 0 would mean the person is truthful.

41. Drawbacks of using micro-expressions • Difficult to spot and analyze manually. • Requires high processing powers to capture and analyze micro-gestures.

42. Fooling the detectors • prepare yourself in advance by thinking about what confessions they are looking for, that you can know what things to admit and what things to deny.

43. Conclusion • What did we discuss? • Lie Detection • Human Methods • Techniques used • Silent Talker Design • Controversy • Though Lie Detectors are not completely accepted by the scientific community, the day might not be far away…

44. References • Bhattacharjee, Anwesha "An Efficient Lie Detector Using FNN", at the IEEE 7th Student Conference on Research and Development, University Putra Malaysia, 2009. https://sites.google.com/site/fnnliedetector/ • Charting the behavioural state of a person using a back propagation neural network, Janet Rothwell, ZuhairBandar, James O’Shea, David McLean. 2009 • Simple Lie Detector - http://www.aaroncake.net/circuits/lie.asp • http://sosuave.net/forum/showthread.php?t=166543 • Use of Fuzzy Set Classification for Pattern Recognition of Polygraph. Knapp, Ulka, jacobs, 1995.

45. Thank you