Counter-forensics in machine learning based forgery detection

Francesco Marra; Giovanni Poggi; Fabio Roli; Carlo Sansone; Luisa Verdoliva

doi:10.1117/12.2182173

4 March 2015 Counter-forensics in machine learning based forgery detection

Francesco Marra, Giovanni Poggi, Fabio Roli, Carlo Sansone, Luisa Verdoliva

Proceedings Volume 9409, Media Watermarking, Security, and Forensics 2015; 94090L (2015) https://doi.org/10.1117/12.2182173
Event: SPIE/IS&T Electronic Imaging, 2015, San Francisco, California, United States

Abstract

With the powerful image editing tools available today, it is very easy to create forgeries without leaving visible traces. Boundaries between host image and forgery can be concealed, illumination changed, and so on, in a naive form of counter-forensics. For this reason, most modern techniques for forgery detection rely on the statistical distribution of micro-patterns, enhanced through high-level filtering, and summarized in some image descriptor used for the final classification. In this work we propose a strategy to modify the forged image at the level of micro-patterns to fool a state-of-the-art forgery detector. Then, we investigate on the effectiveness of the proposed strategy as a function of the level of knowledge on the forgery detection algorithm. Experiments show this approach to be quite effective especially if a good prior knowledge on the detector is available.

Citation Download Citation

Francesco Marra, Giovanni Poggi, Fabio Roli, Carlo Sansone, and Luisa Verdoliva "Counter-forensics in machine learning based forgery detection", Proc. SPIE 9409, Media Watermarking, Security, and Forensics 2015, 94090L (4 March 2015); https://doi.org/10.1117/12.2182173

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