Countering anti-forensics by means of data fusion

Marco Fontani; Alessandro Bonchi; Alessandro Piva; Mauro Barni

doi:10.1117/12.2039569

19 February 2014 Countering anti-forensics by means of data fusion

Marco Fontani, Alessandro Bonchi, Alessandro Piva, Mauro Barni

Proceedings Volume 9028, Media Watermarking, Security, and Forensics 2014; 90280Z (2014) https://doi.org/10.1117/12.2039569
Event: IS&T/SPIE Electronic Imaging, 2014, San Francisco, California, United States

Abstract

In the last years many image forensic (IF) algorithms have been proposed to reveal traces of processing or tampering. On the other hand, Anti-Forensic (AF) tools have also been developed to help the forger in removing editing footprints. Inspired by the fact that it is much harder to commit a perfect crime when the forensic analyst uses a multi-clue investigation strategy, we analyse the possibility o ered by the adoption of a data fusion framework in a Counter-Anti-Forensic (CAF) scenario. We do so by adopting a theoretical framework, based on Dempster-Shafer Theory of Evidence, to synergically merge information provided by IF tools and CAF tools, whose goal is to reveal traces introduced by anti-forensic algorithms. The proposed system accounts for the non-trivial relationships between IF and CAF techniques; for example, in some cases the outputs from the former are expected to contradict the output from the latter. We evaluate the proposed method within a representative forensic task, that is splicing detection in JPEG images, with the forger trying to conceal traces using two di erent counter-forensic methods. Results show that decision fusion strongly limits the e ectiveness of AF methods.

Citation Download Citation

Marco Fontani, Alessandro Bonchi, Alessandro Piva, and Mauro Barni "Countering anti-forensics by means of data fusion", Proc. SPIE 9028, Media Watermarking, Security, and Forensics 2014, 90280Z (19 February 2014); https://doi.org/10.1117/12.2039569

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available