Double-crossing Benford's law | ||
| Journal of Statistical Modelling: Theory and Applications | ||
| دوره 6، شماره 1، فروردین 2025، صفحه 49-57 اصل مقاله (370.93 K) | ||
| نوع مقاله: Original Scientific Paper | ||
| شناسه دیجیتال (DOI): 10.22034/jsmta.2025.22275.1162 | ||
| نویسنده | ||
| Javad Kazemitabar* | ||
| Department of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol, Mazandaran, Iran | ||
| چکیده | ||
| From Covid-19 mortality rate to image tampering, Benford's law is used to detect fraudulent activities. The underlying assumption for using the law is that a ``regular" dataset follows the significant digit phenomenon. In this paper, we address the scenario where a shrewd fraudster manipulates a list of numbers in such a way that while providing the desired statistics, it still complies with Benford's law. We develop a framework that offers several degrees of freedom to such a fraudster, such as the minimum, maximum, mean, and size of the manipulated dataset. The conclusion further corroborates the idea that Benford's law -if at all- should be used with utmost discretion as a means for fraud detection. | ||
| کلیدواژهها | ||
| Benford's law؛ Distribution؛ Forensic؛ Fraud detection؛ Statistical analysis | ||
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آمار تعداد مشاهده مقاله: 133 تعداد دریافت فایل اصل مقاله: 63 |
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