A Comparative Analysis of Forgery Detection Algorithms

The aim of this work is to make an objective comparison between different forgery techniques and present a tool that helps taking a more reliable decision about the integrity of a given image or part of it. The considered techniques, all recently proposed in the scientific community, follow different and complementary approaches so as to guarantee robustness with respect to tampering of different types and characteristics. Experiments have been conducted on a large set of images using an automatic copy-paste tampering generator. Early results point out significant differences about competing techniques, depending also on complexity and side information.

[1]  Mo Chen,et al.  Determining Image Origin and Integrity Using Sensor Noise , 2008, IEEE Transactions on Information Forensics and Security.

[2]  Rainer Böhme,et al.  The 'Dresden Image Database' for benchmarking digital image forensics , 2010, SAC '10.

[3]  Ludmila I. Kuncheva,et al.  Combining Pattern Classifiers: Methods and Algorithms , 2004 .

[4]  Subhash C. Bagui,et al.  Combining Pattern Classifiers: Methods and Algorithms , 2005, Technometrics.

[5]  Hongbin Zhang,et al.  Exposing Digital Image Forgeries by Using Canonical Correlation Analysis , 2010, 2010 20th International Conference on Pattern Recognition.

[6]  Nenghai Yu,et al.  Passive detection of doctored JPEG image via block artifact grid extraction , 2009, Signal Process..

[7]  L. Verdoliva,et al.  PRNU-based detection of small-size image forgeries , 2011, 2011 17th International Conference on Digital Signal Processing (DSP).

[8]  Luisa Verdoliva,et al.  On the influence of denoising in PRNU based forgery detection , 2010, MiFor '10.

[9]  Hany Farid,et al.  Exposing digital forgeries by detecting traces of resampling , 2005 .

[10]  Sebastiano Battiato,et al.  Digital forgery estimation into DCT domain: a critical analysis , 2009, MiFor '09.

[11]  H. Farid,et al.  Image forgery detection , 2009, IEEE Signal Processing Magazine.