Understanding Benford's law and its vulnerability in image forensics

Authors:
Jingwei Wang;Byung-Ho Cha;Seong-Ho Cho;C.-C. Jay Kuo
Affiliations:
Signal and Image Processing Institute and Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, CA;Signal and Image Processing Institute and Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, CA;Signal and Image Processing Institute and Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, CA;Signal and Image Processing Institute and Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, CA
Venue:
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Year:
2009

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Abstract

In this paper, we attempt to shed light on Benford's law from the viewpoint of probability theory and point out its limitation in image forensic applications. First, we consider a generalized form of Benford's law and relate it to a random variable of a certain probability density function such as the generalized Gaussian or Laplacian function. Then, we examine the application of the generalized Benford's law to image forensic applications and point out such an application is vulnerable to the histogram manipulation attack.