Anti-forensics of chromatic aberration

Over the past decade, a number of information forensic techniques have been developed to identify digital image manipulation and falsification. Recent research has shown, however, that an intelligent forger can use anti-forensic countermeasures to disguise their forgeries. In this paper, an anti-forensic technique is proposed to falsify the lateral chromatic aberration present in a digital image. Lateral chromatic aberration corresponds to the relative contraction or expansion between an image's color channels that occurs due to a lens's inability to focus all wavelengths of light on the same point. Previous work has used localized inconsistencies in an image's chromatic aberration to expose cut-and-paste image forgeries. The anti-forensic technique presented in this paper operates by estimating the expected lateral chromatic aberration at an image location, then removing deviations from this estimate caused by tampering or falsification. Experimental results are presented that demonstrate that our anti-forensic technique can be used to effectively disguise evidence of an image forgery.

[1]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[2]  Hany Farid,et al.  Exposing digital forgeries through chromatic aberration , 2006, MM&Sec '06.

[3]  Rainer Böhme,et al.  Synthesis of color filter array pattern in digital images , 2009, Electronic Imaging.

[4]  K. J. Ray Liu,et al.  On Antiforensic Concealability With Rate-Distortion Tradeoff , 2015, IEEE Transactions on Image Processing.

[5]  Rainer Böhme,et al.  Hiding Traces of Resampling in Digital Images , 2008, IEEE Transactions on Information Forensics and Security.

[6]  K. J. Ray Liu,et al.  Anti-forensics of digital image compression , 2011, IEEE Transactions on Information Forensics and Security.

[7]  Mauro Barni,et al.  Forensic Analysis of SIFT Keypoint Removal and Injection , 2014, IEEE Transactions on Information Forensics and Security.

[8]  Min Wu,et al.  Information Forensics: An Overview of the First Decade , 2013, IEEE Access.

[9]  K. J. Ray Liu,et al.  Anti-forensics for frame deletion/addition in MPEG video , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[10]  Fernando Pérez-González,et al.  Optimal counterforensics for histogram-based forensics , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[11]  Mauro Barni,et al.  A universal technique to hide traces of histogram-based image manipulations , 2012, MM&Sec '12.

[12]  Rainer Böhme,et al.  Can we trust digital image forensics? , 2007, ACM Multimedia.

[13]  K. J. Ray Liu,et al.  Anti-forensics of median filtering , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[14]  Thomas Gloe,et al.  Efficient estimation and large-scale evaluation of lateral chromatic aberration for digital image forensics , 2010, Electronic Imaging.

[15]  K. J. Ray Liu,et al.  Temporal Forensics and Anti-Forensics for Motion Compensated Video , 2012, IEEE Transactions on Information Forensics and Security.