Normalized Energy Density-Based Forensic Detection of Resampled Images

We propose a new method to detect resampled imagery. The method is based on examining the normalized energy density present within windows of varying size in the second derivative of the image in the frequency domain, and exploiting this characteristic to derive a 19-D feature vector that is used to train a SVM classifier. Experimental results are reported on 7500 raw images from the BOSS database. Comparison with prior work reveals that the proposed algorithm performs similarly for resampling rates greater than 1, and is superior to prior work for resampling rates less than 1. Experiments are performed for both bilinear and bicubic interpolations, and qualitatively similar results are observed for each. Results are also provided for the detection of resampled imagery with noise corruption and JPEG compression. As expected, some degradation in performance is observed as the noise increases or the JPEG quality factor declines.

[1]  Tomás Pevný,et al.  "Break Our Steganographic System": The Ins and Outs of Organizing BOSS , 2011, Information Hiding.

[2]  David Vazquez-Padin,et al.  Prefilter design for forensic resampling estimation , 2011, 2011 IEEE International Workshop on Information Forensics and Security.

[3]  Tom Fawcett,et al.  ROC Graphs: Notes and Practical Considerations for Researchers , 2007 .

[4]  Xilin Chen,et al.  Automatic detection of signs with affine transformation , 2002, Sixth IEEE Workshop on Applications of Computer Vision, 2002. (WACV 2002). Proceedings..

[5]  Cordelia Schmid,et al.  A Comparison of Affine Region Detectors , 2005, International Journal of Computer Vision.

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

[7]  Babak Mahdian,et al.  Ieee Transactions on Information Forensics and Security 1 Blind Authentication Using Periodic Properties of Interpolation , 2022 .

[8]  Gwenaël J. Doërr,et al.  JPEG recompression detection , 2010, Electronic Imaging.

[9]  Matthias Kirchner,et al.  On resampling detection in re-compressed images , 2009, 2009 First IEEE International Workshop on Information Forensics and Security (WIFS).

[10]  Matthias Kirchner,et al.  Linear row and column predictors for the analysis of resized images , 2010, MM&Sec '10.

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

[12]  Mauro Barni,et al.  Detection of resampled images: Performance analysis and practical challenges , 2010, 2010 18th European Signal Processing Conference.

[13]  Fernando Pérez-González,et al.  On the role of differentiation for resampling detection , 2010, 2010 IEEE International Conference on Image Processing.

[14]  Weihong Wang,et al.  Exposing digital forgeries in video by detecting double MPEG compression , 2006, MM&Sec '06.

[15]  Matthias Kirchner,et al.  Fast and reliable resampling detection by spectral analysis of fixed linear predictor residue , 2008, MM&Sec '08.

[16]  Jan Lukás,et al.  Estimation of Primary Quantization Matrix in Double Compressed JPEG Images , 2003 .

[17]  Ingemar J. Cox,et al.  Digital Watermarking and Steganography , 2014 .

[18]  J. Fridrich,et al.  Detection of double-compression for applications in steganography , 2007 .

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

[20]  Andrew C. Gallagher.  Detection of linear and cubic interpolation in JPEG compressed images , 2005, The 2nd Canadian Conference on Computer and Robot Vision (CRV'05).

[21]  Qingzhong Liu,et al.  A new approach for JPEG resize and image splicing detection , 2009, MiFor '09.

[22]  Tomás Pevný,et al.  Detection of Double-Compression in JPEG Images for Applications in Steganography , 2008, IEEE Transactions on Information Forensics and Security.

[23]  Ingemar J. Cox,et al.  An energy-based method for the forensic detection of Re-sampled images , 2011, 2011 IEEE International Conference on Multimedia and Expo.