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Proceedings Paper

Counter-forensics in machine learning based forgery detection
Author(s): Francesco Marra; Giovanni Poggi; Fabio Roli; Carlo Sansone; Luisa Verdoliva
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Paper 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.

Paper Details

Date Published: 4 March 2015
PDF: 11 pages
Proc. SPIE 9409, Media Watermarking, Security, and Forensics 2015, 94090L (4 March 2015); doi: 10.1117/12.2182173
Show Author Affiliations
Francesco Marra, Univ. degli Studi di Cagliari (Italy)
Univ. degli Studi di Napoli Federico II (Italy)
Giovanni Poggi, Univ. degli Studi di Napoli Federico II (Italy)
Fabio Roli, Univ. degli Studi di Cagliari (Italy)
Carlo Sansone, Univ. degli Studi di Napoli Federico II (Italy)
Luisa Verdoliva, Univ. degli Studi di Napoli Federico II (Italy)


Published in SPIE Proceedings Vol. 9409:
Media Watermarking, Security, and Forensics 2015
Adnan M. Alattar; Nasir D. Memon; Chad D. Heitzenrater, Editor(s)

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