Exposing digital forgeries in color filter array interpolated images

With the advent of low-cost and high-resolution digital cameras, and sophisticated photo editing software, digital images can be easily manipulated and altered. Although good forgeries may leave no visual clues of having been tampered with, they may, nevertheless, alter the underlying statistics of an image. Most digital cameras, for example, employ a single sensor in conjunction with a color filter array (CFA), and then interpolate the missing color samples to obtain a three channel color image. This interpolation introduces specific correlations which are likely to be destroyed when tampering with an image. We quantify the specific correlations introduced by CFA interpolation, and describe how these correlations, or lack thereof, can be automatically detected in any portion of an image. We show the efficacy of this approach in revealing traces of digital tampering in lossless and lossy compressed color images interpolated with several different CFA algorithms.

[1]  Siwei Lyu,et al.  Higher-order Wavelet Statistics and their Application to Digital Forensics , 2003, 2003 Conference on Computer Vision and Pattern Recognition Workshop.

[2]  Jessica Fridrich,et al.  Detection of Copy-Move Forgery in Digital Images , 2004 .

[3]  Thomas W. Parks,et al.  Adaptive homogeneity-directed demosaicing algorithm , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[4]  Stephan Katzenbeisser,et al.  Information Hiding Techniques for Steganography and Digital Watermaking , 1999 .

[5]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[6]  Alin C. Popescu,et al.  Exposing Digital Forgeries by Detecting Duplicated Image Regions Exposing Digital Forgeries by Detecting Duplicated Image Regions , 2004 .

[7]  Wesley E. Snyder,et al.  Demosaicking methods for Bayer color arrays , 2002, J. Electronic Imaging.

[8]  Min Wu,et al.  Reading Between the Lines: Lessons from the SDMI Challenge , 2001, USENIX Security Symposium.

[9]  Hany Farid,et al.  Exposing digital forgeries by detecting traces of resampling , 2005, IEEE Transactions on Signal Processing.

[10]  Mahalingam Ramkumar,et al.  A classifier design for detecting image manipulations , 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..

[11]  Yücel Altunbasak,et al.  Color plane interpolation using alternating projections , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[12]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[13]  Thomas W. Parks,et al.  Adaptively quadratic (AQua) image interpolation , 2004, IEEE Transactions on Image Processing.

[14]  L. R. Connor Urban Housing in England and Wales. , 1936 .

[15]  R. Keys Cubic convolution interpolation for digital image processing , 1981 .

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

[17]  Edward Y. Chang,et al.  Color filter array recovery using a threshold-based variable number of gradients , 1999, Electronic Imaging.

[18]  Hany Farid,et al.  Statistical Tools for Digital Forensics , 2004, Information Hiding.

[19]  Ingemar J. Cox,et al.  Digital Watermarking , 2003, Lecture Notes in Computer Science.

[20]  R. Fisher THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

[21]  Nasir D. Memon,et al.  Blind source camera identification , 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..