Exposing Image Tampering with the Same Quantization Matrix

Image tampering, being readily facilitated and proliferated by today’s digital techniques, is increasingly causing problems regarding the authenticity of images. As the most popular multimedia data, JPEG images can be easily tampered without leaving any clues; therefore, JPEG-based forensics , including the detection of double compression, interpolation, rotation, etc., has become an active research topic in multimedia forensics. Nevertheless, the interesting issue of detecting image tampering and its related operations by using the same quantization matrix has not been fully investigated. Aiming to detect such forgery manipulations under the same quantization matrix, we propose a detection method by using shift-recompression -based reshuffle characteristic features. Learning classifiers are applied to evaluating the efficacy. Our experimental results indicate that the approach is indeed highly effective in detecting image tampering and relevant manipulations with the same quantization matrix.

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

[2]  Qingzhong Liu,et al.  Temporal Derivative-Based Spectrum and Mel-Cepstrum Audio Steganalysis , 2009, IEEE Transactions on Information Forensics and Security.

[3]  Y.-L. Chen,et al.  Detecting Recompression of JPEG Images via Periodicity Analysis of Compression Artifacts for Tampering Detection , 2011, IEEE Transactions on Information Forensics and Security.

[4]  Alin C. Popescu,et al.  Exposing digital forgeries in color filter array interpolated images , 2005, IEEE Transactions on Signal Processing.

[5]  Xunyu Pan,et al.  Region Duplication Detection Using Image Feature Matching , 2010, IEEE Transactions on Information Forensics and Security.

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

[7]  Yun Q. Shi,et al.  JPEG image steganalysis utilizing both intrablock and interblock correlations , 2008, 2008 IEEE International Symposium on Circuits and Systems.

[8]  Gaurav Sharma,et al.  Detecting content adaptive scaling of images for forensic applications , 2010, Electronic Imaging.

[9]  Wei Su,et al.  Image splicing detection using 2-D phase congruency and statistical moments of characteristic function , 2007, Electronic Imaging.

[10]  S. Prasad,et al.  On Resampling Detection and its Application to Detect Image Tampering , 2006, 2006 IEEE International Conference on Multimedia and Expo.

[11]  Qingzhong Liu,et al.  Detection of misaligned cropping and recompression with the same quantization matrix and relevant forgery , 2011, MiFor '11.

[12]  Nasir D. Memon,et al.  Image tamper detection based on demosaicing artifacts , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

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

[14]  Jessica J. Fridrich,et al.  Steganalysis in high dimensions: fusing classifiers built on random subspaces , 2011, Electronic Imaging.

[15]  Jessica J. Fridrich,et al.  Steganalysis of JPEG images using rich models , 2012, Other Conferences.

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

[17]  Anindya Sarkar,et al.  Detection of seam carving and localization of seam insertions in digital images , 2009, MM&Sec '09.

[18]  Hany Farid,et al.  Exposing Digital Forgeries From JPEG Ghosts , 2009, IEEE Transactions on Information Forensics and Security.

[19]  Hany Farid,et al.  Detecting Digital Forgeries Using Bispectral Analysis , 1999 .

[20]  J. Hilbe.  Logistic Regression Models , 2009 .

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

[22]  Qingzhong Liu,et al.  Improved detection and evaluation for JPEG steganalysis , 2009, ACM Multimedia.

[23]  Jiwu Huang,et al.  A Novel Method for Detecting Cropped and Recompressed Image Block , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[24]  Qingzhong Liu,et al.  A Method to Detect JPEG-Based Double Compression , 2011, ISNN.

[25]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[26]  Qingzhong Liu,et al.  Neighboring joint density-based JPEG steganalysis , 2011, TIST.

[27]  Qingzhong Liu,et al.  Improved Approaches with Calibrated Neighboring Joint Density to Steganalysis and Seam-Carved Forgery Detection in JPEG Images , 2014, ACM Trans. Intell. Syst. Technol..

[28]  Qingzhong Liu,et al.  Novel stream mining for audio steganalysis , 2009, MM '09.

[29]  Lawrence Carin,et al.  Sparse multinomial logistic regression: fast algorithms and generalization bounds , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[30]  Nasir D. Memon,et al.  An efficient and robust method for detecting copy-move forgery , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[31]  Thorsten Joachims,et al.  Estimating the Generalization Performance of an SVM Efficiently , 2000, ICML.

[32]  Jessica J. Fridrich,et al.  Rich Models for Steganalysis of Digital Images , 2012, IEEE Transactions on Information Forensics and Security.

[33]  Hany Farid,et al.  Digital Image Ballistics from JPEG Quantization , 2006 .

[34]  Yun Q. Shi,et al.  A Markov Process Based Approach to Effective Attacking JPEG Steganography , 2006, Information Hiding.

[35]  Qingzhong Liu,et al.  Derivative-based audio steganalysis , 2011, TOMCCAP.

[36]  Jessica J. Fridrich,et al.  Calibration revisited , 2009, MM&Sec '09.

[37]  Tomás Pevný,et al.  Merging Markov and DCT features for multi-class JPEG steganalysis , 2007, Electronic Imaging.

[38]  S. Avidan,et al.  Seam carving for content-aware image resizing , 2007, SIGGRAPH 2007.

[39]  Qingzhong Liu,et al.  Steganalysis of DCT-embedding based adaptive steganography and YASS , 2011, MM&Sec '11.