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Pahuja, D (2021). Application of Benford’s Law to Detect if COVID-19 Data is under Reported or Manipulated. In: Rahul Srivastava & Aditya Kumar Singh Pundir (eds.), New Frontiers in Communication and Intelligent Systems, pp. 85–91. Computing & Intelligent Systems, SCRS, India .

This work cites the following items of the Benford Online Bibliography:

Amouzegar, MA, Moshirvaziri, K and Snyder, D (2018). Benford's Law And Its Application To Modern Information Security. Proceedings of 47th Annual Meeting of WDSI (Western Decision Sciences Institute) . View Complete Reference Online information Works that this work references Works that reference this work
Chatziapostolou, E (2019). Fraud detection using Benford’s Law (Python Code). Published in Towards Data Science, August 12. View Complete Reference Online information No Bibliography works referenced by this work. Works that reference this work
Coeurjolly, J-F (2020). Digit analysis for Covid-19 reported data . Preprint arXiv:2005.05009 [stat.AP]; last accessed May 17, 2020. View Complete Reference Online information Works that this work references Works that reference this work
Fu, D, Shi, YQ and Su, W (2007). A generalized Benford’s law for JPEG coefficients and its applications in image forensics. Proceedings of SPIE, Volume 6505, Security, Steganography and Watermarking of Multimedia Contents IX, San Jose, California, January 28 - February 1, 2007, pp. 65051L-65051L-11. DOI:10.1117/12.704723. View Complete Reference Online information Works that this work references Works that reference this work
Gamermann, D and Antunes, FL (2018). Statistical analysis of Brazilian electoral campaigns via Benford’s law. Physica A: Statistical Mechanics and its Applications 496, pp. 171-188. DOI:10.1016/j.physa.2017.12.120. View Complete Reference Online information Works that this work references Works that reference this work
Golbeck, J (2019). Benford’s Law can detect malicious social bots. First Monday 24(8). DOI:10.5210/fm.v24i8.10163. View Complete Reference Online information Works that this work references Works that reference this work
Iorliam, A and Shangbum, FC (2017). On the Use of Benford’s Law to Detect JPEG Biometric Data Tampering. Journal of Information Security 8, pp. 240-256. DOI:10.4236/jis.2017.83016. View Complete Reference Online information Works that this work references Works that reference this work
Koch, C and Okamura, K (2020). Benford's Law and COVID-19 Reporting. Posted on SSRN April 28, 2020; last accessed November 17, 2020. Published in Econ Lett 2020;196(109973) . View Complete Reference Online information Works that this work references Works that reference this work
Lanham, SW (2019). Analyzing Big Data with Benford’s Law: A Lesson for the Classroom. American Journal of Business Education 12(2), pp. 33-42. DOI:10.19030/ajbe.v12i2.10285. View Complete Reference Online information Works that this work references Works that reference this work
Lee, K-B, Han, S and Jeong, Y (2020). COVID-19, flattening the curve, and Benford’s law. Physica A: Statistical Mechanics and its Applications 559, 125090. DOI:10.1016/j.physa.2020.125090. View Complete Reference Online information Works that this work references Works that reference this work
Li, F, Han, S, Zhang, H, Ding, J, Zhang, J and Wu, J (2019). Application of Benford’s law in Data Analysis. Journal of Physics: Conference Series 1168, pp. 032133. DOI:10.1088/1742-6596/1168/3/032133. View Complete Reference Online information Works that this work references Works that reference this work
Moreau, VH (2021). Inconsistencies in Countries COVID-19 Data Revealed by Benford’s Law’. Model Assisted Statistics and Applications 16(1), pp. 73-79. DOI:10.3233/MAS-210517. View Complete Reference Online information Works that this work references Works that reference this work
Nigrini, MJ (1996). A taxpayer compliance application of Benford’s law. Journal of the American Taxation Association 18(1), pp. 72-91. View Complete Reference Online information Works that this work references Works that reference this work
Sun, L, Ho, ATS, Xia, Z, Chen, J, Huang, X and Zhang, Y (2017). Detection and Classification of Malicious Patterns In Network Traffic Using Benford’s Law. 2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), Kuala Lumpur, pp. 864-872. DOI:10.1109/APSIPA.2017.8282154. View Complete Reference Online information Works that this work references Works that reference this work
Yong-wook, K, Han, J and Seoung-rak, C (2018). Detection of Possible Match-fixing in Tennis Games. In Proceedings of the 6th International Congress on Sport Sciences Research and Technology Support (icSPORTS 2018), pp. 124-131. DOI: 10.5220/0006924201240131. View Complete Reference Online information Works that this work references Works that reference this work