Features of Signature Verification By a Person as a Primary Criteria for Developing an Artificial Intelligence System

The modern capabilities of computers have returned interest in artificial intelligence technologies. A particular area of application of these technologies is pattern recognition, which can be applied to the traditional forensic task – identification of signs of forgery (imitation) of a signature. The results of forgery are differentiated into three types: auto-forgery, simple and skilled forgeries. Only skilled forgeries are considered in this study. The online and offline approaches to the study of signatures and other handwriting material are described. The developed artificial intelligence system based on an artificial neural network refers to the offline type of signature recognition – that is, it is focused on working exclusively with the consequences of the signature – its graphic image. The content and principles of the formation of a hypothesis for the development of an artificial intelligence system are described with a combination of humanitarian (legal) knowledge and natural-technical knowledge. At the initial stage of the study, in order to develop an experimental-applied artificial intelligence system based on an artificial neural network focused on identifying forged signatures, 127 people were questioned in order to identify a person's ability to detect fake signatures. It was found that under experimental conditions the probability of a correct determination of the originality or forgery of the presented signature for the respondent is on average 69.29 %. Accordingly, this value can be used as a threshold for determining the effectiveness of the developed artificial intelligence system. In the process of preparing the dataset (an array for training and verification of its results) of the system in terms of fraudulent signatures, some forensically significant features were revealed, associated with the psychological and anatomical features of the person performing the forgery, both known to criminalistics and new ones. It is emphasized that the joint development of artificial intelligence systems by the methods of computer science and criminalistics can generate additional results that may be useful outside the scope of the research tasks.

artificial intelligence, artificial neural networks, offline signature recognition, signature forgery, handwriting examination, signature

1. Kozochkin V. M., Rybalkin N. A. Sudebno-pocherkovedcheskaya ekspertiza podpisei, vypolnennykh s podrazhaniem podpisyam drugogo litsa [Forensic Handwriting Examination Signatures Made Imitated Signatures Other Person]. Izvestiya Tul’skogo gosudarstvennogo universiteta. Ekonomicheskie i yuridicheskie nauki – News of the Tula State University. Economic and Legal Sciences, 2016, no. 4-2, pp. 125–130.

2. Fedorovich V. Yu. Kriminalisticheskoe issledovanie podpisi [Forensic Signature Research]. Moscow, Yurait Publ., 2020. 142 p.

3. Okhlupina A. N. Teoreticheskie, metodicheskie i organizatsionno-takticheskie osnovy primeneniya intellektual’nykh sistem v sudebno-pocherkovedcheskom issledovanii podpisei. Dis. kand. yurid. nauk [Theoretical, Methodological and Organizational-Tactical Foundations of the Application of Intelligent Systems in The Forensic Handwriting Study of Signatures. Cand. Legal Sci. Dis.]. Moscow, 2019. 192 p.

4. Arkhipov V. V. Regulirovanie robototekhniki: vvedenie v «robopravo». Pravovye aspekty razvitiya robototekhniki i tekhnologii iskusstvennogo intellekta [Regulation of Robotics: An Introduction to the “Robo-Law”. Legal Aspects of the Development of Robotics and Artificial Intelligence Technologies]. Moscow, Infotropik Media Publ., 2018. 232 p.

5. Timofeev A. V. Roboty i iskusstvennyi intellect [Robots and Artificial Intelligence]. Moscow, The Main Editorial Office of Physical and Mathematical Literature of the Nauka Publ., 1978. 192 p.

6. Fedotov A. Yu. Problemy nadezhnosti v ramkakh razlichnykh tipov nauchnoi ratsional’nosti [Problems of Reliability in the Context of Various Types of Scientific Rationality]. Psikhopedagogika v pravookhranitel’nykh organakh – Psychopedagogics in Law Enforcement, 2017, no. 4 (71), pp. 24–28.

7. Hafemann L. G., Sabourin R., Oliveira L. S. Offline Handwritten Signature Verification – Literature Review. 2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA). IEEE, 2017. P. 1–8. DOI: 10.1109/IPTA.2017.8310112.

8. Ortega-Garcia J., Fierrez-Aguilar J., Simon D., Gonzalez J., Faundez-Zanuy M., Espinosa V., Satue A., Hernaez I., Igarza J.-J., Vivaracho C., Escudero D., Moro Q.-I. MCYT Baseline Corpus: A Bimodal Biometric Database. IEE Proceedings – Vision, Image and Signal Processing, 2003, vol. 150, iss. 6, pp. 395–401. DOI: 10.1049/ip-vis:20031078.

9. Vargas F., Ferrer M., Travieso C., Alonso J. Off-line Handwritten Signature GPDS-960 Corpus. Proceedings of the International Conference on Document Analysis and Recognition (ICDAR 2007). Parana, 2007, pp. 764–768. DOI: 10.1109/ICDAR.2007.4377018.

10. Radhika K. S., Gopika S. Online and Offline Signature Verification: A Combined Approach. Procedia Computer Science, 2015, vol. 46, pp. 1593–1600. DOI: 10.1016/j.procs.2015.02.089.

11. Dey S., Dutta A., Toledo J., Ghosh S., Lladós J., Pal U. SigNet: Convolutional Siamese Network for Writer Independent Offline Signature Verification. URL: https://arxiv.org/abs/1707.02131