Abstract
Deceit identification has been a problem since ages and for generations past now. The process is taken in both positive and negative prespectives. Positive for the justice it delivers to the unjustified people and negative for the techniques that are used for the purpose. It has been historically proved that the techniques involved in lie detection (common term used for deceit identification) involve many methodologies including those against human rights and various international conventions. The processes involved have always been in a questions and various studies have tried to prove the deceit; hence, latest methodologies and developments in machine learning area are under trial for such detection jobs. The following paper discusses various supervised learning methodologies like k-Nearest Neighbours, AdaBoost, etc., in order to prove deceit. As general awareness the deceit information used for supervised learning is already labelled and is taken from an open-source dataset. The study tries to establish a basic threshold of parameters in lie detection (LD) and is comparable to the human levels of information gathering with deceit identification with an accuracy of over 70% which is comparable to that of human interrogation techniques. The paper aims to lay a basis for deceit detection using machine learning methodologies which in the future could change the face of LD.
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References
Cacioppo JT, Tassinary LG, Berntson G (eds) (2007) Handbook of psychophysiology, 3rd ed. Cambridge University Press, Cambridge
Gao J, Wang Z, Yang Y, Zhang W, Tao C et al (2013) A novel approach for lie detection based on F-score and extreme learning machine. PLoS ONE 8(6):e64704. https://doi.org/10.1371/journal.pone.0064704
Rosenfeld JP (2002) Event-related potentials in the detection of deception. Handbook of polygraph testing. Academic Press, New York, pp 265–286
Mohammed IJ, George LE (2022) Lie detection and truth identification form EEG signals by using frequency and time features. J Algeb Stat 13(3):4102–4121
Saini N, Bhardwaj S, Agarwal R (2020) Classification of EEG signals using hybrid combination of features for lie detection. Neural Comput Appl 32:3777–3787. https://doi.org/10.1007/s00521-019-04078-z
Demiralp T, Yordanova J, Kolev V, Ademoglu A, Devrim M, Samar VJ (1999) Time-frequency analysis of single-sweep event-related potentials by means of fast wavelet transform. Brain Lang 66(1):129–145. https://doi.org/10.1006/brln.1998.2028. PMID: 10080868
Gao J et al (2015) Data from: a novel algorithm to enhance P300 in single trials: application to lie detection using F-score and SVM. Dryad Dataset. https://doi.org/10.5061/dryad.2qc64
Ibrahim YA, Odiketa JC, Ibiyemi TS (2017) Preprocessing technique in automatic speech recognition for human computer interaction: An overview. Ann Comput Sci Inf Syst 15(1):186–191
Gouyon F, Pachet F, Delerue O et al (2000) On the use of zero-crossing rate for an application of classification of percussive sounds. In: Proceedings of the COST G-6 conference on digital audio effects (DAFX-00), Verona, Italy, vol 5. Citeseer, p 16
Gao J, Tian H, Yang Y, Yu X, Li C et al (2014) A novel algorithm to enhance P300 in single trials: application to lie detection using F-Score and SVM. PLoS ONE 9(11):e109700. https://doi.org/10.1371/journal.pone.0109700
Edla DR, Dodia S, Bablani A, Kuppili V (2021) An efficient deep learning paradigm for deceit identification test on EEG signals. ACM Trans Manag Inf Syst 12(3):1–20. https://doi.org/10.1145/3458791
Baghel N, Singh D, Dutta MK, Burget R, Myska V (2020) Truth Identification from EEG Signal by using Convolution neural network: lie detection. In: 2020 43rd international conference on telecommunications and signal processing (TSP), Milan, Italy, pp 550–553. https://doi.org/10.1109/TSP49548.2020.9163497
Gao J, Rao N, Yang Y, Wei W (2012) A new lie detection method based on small-number of P300 responses. In: 2012 international conference on biomedical engineering and biotechnology. https://doi.org/10.1109/icbeb.2012.31
Tarassenko L, Khan YU, Holt MRG (1998) Identification of interictal spikes in the EEG using neural network analysis. Inst Elect Eng Proc Sci Meas Technol 145:270–278
Shoker L, Sanei S, Chambers J (2005) Artifact removal from electroencephalograms using a hybrid BSS-SVM algorithm. IEEE Signal Process Lett 12:721–724
Bablani A, Edla DR, Kuppili V (2018) Deceit identification test on EEG data using deep belief network. In: 2018 9th international conference on computing, communication and networking technologies (ICCCNT). https://doi.org/10.1109/icccnt.2018.8494124
Lu N, Li T, Ren X, Miao H (2017) A deep learning scheme for motor imagery classification based on restricted Boltzmann machines. IEEE Trans Neural Syst Rehabil Eng 25(6):566–576
Jenke R, Peer A, Buss M (2014) Feature extraction and selection for emotion recognition from EEG. IEEE Trans Affect Comput 5(3):327–339
Farahani ED, Moradi MH (2017) Multimodal detection of concealed information using genetic-SVM classifier with strict validation structure. Inf Med Unlocked
Gonzalez-Billandon J, Aroyo AM, Tonelli A, Pasquali D, Sciutti A, Gori M, Sandini G, Rea F (2019) Can a robot catch you lying? A machine learning system to detect lies during interactions. Front Rob AI 6:64. https://doi.org/10.3389/frobt.2019.00064
Simbolon AI, Turnip A, Hutahaean J, Siagian Y, Irawati N (2015) An experiment of lie detection based EEG-P300 classified by SVM algorithm. In: 2015 international conference on automation, cognitive science, optics, micro electro-mechanical system, and information technology (ICACOMIT). https://doi.org/10.1109/icacomit.2015.7440177
Haider SK, Daud MI, Jiang A, Khan Z (2017) Evaluation of P300 based lie detection algorithm. Electr Electron Eng 7(3):69–76. https://doi.org/10.5923/j.eee.20170703.01
Turnip A, Amri MF, Suhendra MA, Kusumandari DE (2017) Lie detection based EEG-P300 signal classified by ANFIS method. JTEC 9(1–5):107–110
Misra MK, Chaturvedi A, Tripathi SP, Shukla V (2019) A unique key sharing protocol among three users using non-commutative group for electronic health record system. J Disc Math Sci Cryptogr 22(8):1435–1451. https://doi.org/10.1080/09720529.2019.1692450
Shukla V, Chaturvedi A, Misra MK (2021) On authentication schemes using polynomials over non commutative rings. Wirel Pers Commun 118(1):1–9. https://doi.org/10.1007/s11277-020-08008-4
Chaturvedi A, Shukla V, Misra MK (2018) Three party key sharing protocol using polynomial rings. In: 5th IEEE Uttar Pradesh section international conference on electrical, electronics and computer engineering (UPCON), pp 1–5. https://doi.org/10.1109/UPCON.2018.8596905
Shukla V, Misra MK, Chaturvedi A (2022) Journey of cryptocurrency in India in view of financial budget 2022–23. Cornell University, pp 1–6. https://doi.org/10.48550/arXiv.2203.12606
Chaturvedi A, Srivastava N, Shukla V, Tripathi SP, Misra MK (2015) A secure zero knowledge authentication protocol for wireless (mobile) ad-hoc networks. Int J Comput Appl 128(2):36–39. https://doi.org/10.5120/ijca2015906437
Chaturvedi A, Shukla V, Misra MK (2021) A random encoding method for secure data communication: an extension of sequential coding. J Disc Math Sci Cryptogr 24(5):1189–1204. https://doi.org/10.1080/09720529.2021.1932902
Shukla V, Misra MK, Chaturvedi A (2021) A new authentication procedure for client-server applications using HMAC. J Disc Math Sci Cryptogr 24(5):1241–1256. https://doi.org/10.1080/09720529.2021.1932908
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Sharma, S., Gupta, M.K. (2024). Supervised Learning Approaches for Deceit Identification: Exploring EEG as a Non-invasive Technique. In: Chaturvedi, A., Hasan, S.U., Roy, B.K., Tsaban, B. (eds) Cryptology and Network Security with Machine Learning. ICCNSML 2023. Lecture Notes in Networks and Systems, vol 918. Springer, Singapore. https://doi.org/10.1007/978-981-97-0641-9_12
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