Cross Reference Up

Bonettini, N, Bestagini, P, Milani, S and Tubaro, S (2020). On the use of Benford's law to detect GAN-generated images. Preprint arXiv:arXiv:2004.07682 [cs.CV]; last accessed April 21, 2020 (2020 25th International Conference on Pattern Recognition (ICPR), pp. 5495-5502) .

This work is cited by the following items of the Benford Online Bibliography:

Note that this list may be incomplete, and is currently being updated. Please check again at a later date.

Golbeck, J (2023). Benford’s Law applies to word frequency rank in English, German, French, Spanish, and Italian. PLoS ONE 18(9), pp. e0291337. DOI:10.1371/journal.pone.0291337. View Complete Reference Online information Works that this work references No Bibliography works reference this work
Iorliam, A, Emmanual, O and Shehu, YI (2021). An Investigation of "Benford's" Law Divergence and Machine Learning Techniques for "Intra-Class" Separability of Fingerprint Images. Preprint arXiv:2201.01699 [cs.CV]; last accessed January 12, 2022. View Complete Reference Online information Works that this work references No Bibliography works reference this work
Kit, KS, Wong, WK, Chew, IM, Juwono, FH and Sivakumar, S (2023). A Scoping Review of GAN-Generated Images Detection. Proceedings of 2023 International Conference on Digital Applications, Transformation & Economy (ICDATE). DOI:10.1109/ICDATE58146.2023.10248679. View Complete Reference Online information Works that this work references No Bibliography works reference this work
Mari, D, Latora, F and Milani, S (2022). The Sound of Silence: Efficiency of First Digit Features in Synthetic Audio Detection. Preprint arXiv:2210.02746 [cs.SD]; last accessed November 2, 2022. 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
McCarville, D (2021). A data transformation process for using Benford’s Law with bounded data. Preprint [version 1; peer review: awaiting peer review], Emerald Open Research 3(29). DOI:10.35241/emeraldopenres.14374.1. View Complete Reference Online information Works that this work references No Bibliography works reference this work
O'Mahony, L, O'Sullivan, DJP and Nikolov, NS (2023). On the Detection of Anomalous or Out-of-Distribution Data in Vision Models Using Statistical Techniques.. In: Proceedings of the 3rd International Conference on Artificial Intelligence and Computer Vision (AICV2023), March 5–7, 2023. AICV 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 164. Springer, Cham.. DOI:10.1007/978-3-031-27762-7_40. View Complete Reference Online information Works that this work references No Bibliography works reference this work
Parnak, A, Damavandi, YB and Kazemitabar, SJ (2022). A Novel Image Splicing Detection Algorithm based on Generalized and Traditional Benford’s Law. International Journal of Engineering, Transactions A: Basics 35(4), pp. 626-634. View Complete Reference Online information Works that this work references Works that reference this work
Rubin, AE (2021). Benford’s law: Applications to ordinary-chondrite mass distributions. Meteoritics & Planetary Science, pp. 1-14. DOI:10.1111/maps.13626. View Complete Reference Online information Works that this work references No Bibliography works reference this work
Sahu, SK, Java, A and Shaikh, A (2021). On The Connection of Benford’s Law and Neural Networks. Preprint arXiv:2102.03313 [cs.LG]; last accessed February 21, 2021. View Complete Reference Online information Works that this work references No Bibliography works reference this work
Sahu, SK, Java, A and Shaikh, A (2021). Rethinking Neural Networks with Benford’s Law. Proceedings of Fourth Workshop on Machine Learning and the Physical Sciences (NeurIPS 2021). View Complete Reference Online information Works that this work references Works that reference this work
Vishnu, U (2021). Deepfake Detection using Benford’s Law and Distribution Variance Statistic. International Research Journal of Engineering and Technology(IRJET) 08(10), pp. 712-719. View Complete Reference Online information Works that this work references No Bibliography works reference this work