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Satapathy, G, Bhattacharya, G, Puhan, NB and Ho, ATS (2020). Generalized Benford’s Law for Fake Fingerprint Detection. Proceedings of 2020 IEEE Applied Signal Processing Conference (ASPCON), Kolkata, pp. 242-246.

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Fernandes, P and Antunes, M (2023). Benford’s law applied to digital forensic analysis. Forensic Science International: Digital Investigation 45, p. 301515. DOI:10.1016/J.FSIDI.2023.301515. View Complete Reference Online information Works that this work references Works that reference this work
Iorliam, A, Emmanual, O and Shehu, YI (2022). An Investigation of Benford’s Law Divergence and Machine Learning Techniques for Intra-Class Separability of Fingerprint Images. Gazi University Journal of Science Part A: Engineering and Innovation 9(3), pp.211–224 (Preprint arXiv:2201.01699 [cs.CV]; last accessed January 12, 2022). DOI:10.54287/gujsa.1077430. View Complete Reference Online information Works that this work references Works that reference this work
Kobiela, J and Dzierwa, P (2024). Application of Benford’s Law to the Identification of Non-authentic Digital Images. Proceedings of the 22nd International Conference on Advances in Mobile Computing and Multimedia Intelligence, MoMM 2024, held in Bratislava, Slovak Republic, pp. 115-129 . DOI:10.1007/978-3-031-78049-3_12. View Complete Reference Online information Works that this work references No Bibliography works reference this work
Maza-Quiroga, R, Thurnhofer-Hemsi, K, López-Rodríguez, D and López-Rubio, E (2023). Regression of the Rician Noise Level in 3D Magnetic Resonance Images from the Distribution of the First Significant Digit . Axioms 12, pp. 1117 . DOI:10.3390/axioms12121117. View Complete Reference Online information Works that this work references No Bibliography works reference this work