double-crossing benford's law

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.