Scaling factor estimation on JPEG compressed images by cyclostationarity analysis

Scaling factor estimation is one of the most important topics in image forensics. The existing methods mainly employ the peak of the Fourier spectrum of the variance on image difference to detect the scaling factor. However, when the image is compressed, there will be additional stronger peaks which greatly affect the detection ability. In this paper, a novel method to estimate the scaling factor on JPEG compressed images in the presence of image scaling before the compression is proposed. We find the squared image difference can more effectively obtain the resampling characteristics, and we will mathematically show its periodicity. To further improve the detection ability, we analyze the flat block. It also produces periodic peaks in the spectrum, meanwhile which are enhanced by JPEG compression. To solve this problem, a method based on interpolation on the flat block is developed to remove these influences. The experimental results demonstrate that the proposed detection method outperforms some state-of-the-art methods.

[1]  Bin Li,et al.  MSE period based estimation of first quantization step in double compressed JPEG images , 2017, Signal Process. Image Commun..

[2]  Jialiang Chen,et al.  Binary image steganalysis based on local texture pattern , 2018, J. Vis. Commun. Image Represent..

[3]  Giovanni Maria Farinella,et al.  Robust Image Alignment for Tampering Detection , 2012, IEEE Transactions on Information Forensics and Security.

[4]  Alessandro Piva,et al.  Reverse engineering of double JPEG compression in the presence of image resizing , 2012, 2012 IEEE International Workshop on Information Forensics and Security (WIFS).

[5]  Hany Farid,et al.  Exposing digital forgeries by detecting inconsistencies in lighting , 2005, MM&Sec '05.

[6]  V. Mankar,et al.  Blind method for rescaling detection and rescale factor estimation in digital images using periodic properties of interpolation , 2014 .

[7]  Fei Peng,et al.  A reversible watermarking for authenticating 2D vector graphics based on bionic spider web , 2017, Signal Process. Image Commun..

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

[9]  Nenghai Yu,et al.  Image Forensics with Rotation-Tolerant Resampling Detection , 2012, 2012 IEEE International Conference on Multimedia and Expo Workshops.

[10]  David Vazquez-Padin,et al.  ML estimation of the resampling factor , 2012, 2012 IEEE International Workshop on Information Forensics and Security (WIFS).

[11]  David Vazquez-Padin,et al.  Two-dimensional statistical test for the presence of almost cyclostationarity on images , 2010, 2010 IEEE International Conference on Image Processing.

[12]  Nanning Zheng,et al.  Moment feature based forensic detection of resampled digital images , 2013, ACM Multimedia.

[13]  Chun-Wei Wang,et al.  Effective Detection for Linear Up-Sampling by a Factor of Fraction , 2012, IEEE Transactions on Image Processing.

[14]  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).

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

[16]  Rohit Srivastava,et al.  A Comprehensive Survey on Digital Image Watermarking Techniques , 2015, 2015 Fifth International Conference on Communication Systems and Network Technologies.

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

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

[19]  Chinmay A. Vyas,et al.  A review on methods for image authentication and visual cryptography in digital image watermarking , 2014 .

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

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

[22]  David Vazquez-Padin,et al.  An SVD approach to forensic image resampling detection , 2015, 2015 23rd European Signal Processing Conference (EUSIPCO).

[23]  Anindya Sarkar,et al.  Adding Gaussian noise to “denoise” JPEG for detecting image resizing , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[24]  Stefan Katzenbeisser,et al.  Detecting Resized Double JPEG Compressed Images - Using Support Vector Machine , 2013, Communications and Multimedia Security.

[25]  Fan Yang,et al.  Copy-move forgery detection based on hybrid features , 2017, Eng. Appl. Artif. Intell..

[26]  George Wolberg,et al.  Digital image warping , 1990 .

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

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

[29]  Wei Lu,et al.  Joint image splicing detection in DCT and Contourlet transform domain , 2016, J. Vis. Commun. Image Represent..

[30]  Wei Lu,et al.  Region duplication detection based on Harris corner points and step sector statistics , 2013, J. Vis. Commun. Image Represent..

[31]  P. P. Vaidyanathan,et al.  Effects of Multirate Systems on the Statistical Properties of Random Signals , 1993, IEEE Trans. Signal Process..

[32]  Fernando Pérez-González,et al.  A Random Matrix Approach to the Forensic Analysis of Upscaled Images , 2017, IEEE Transactions on Information Forensics and Security.

[33]  Wei Lu,et al.  An Image Region Description Method Based on Step Sector Statistics and its Application in Image Copy-Rotate/Flip-Move Forgery Detection , 2012, Int. J. Digit. Crime Forensics.

[34]  Xinfeng Zhang,et al.  Joint Feature and Texture Coding: Toward Smart Video Representation via Front-End Intelligence , 2019, IEEE Transactions on Circuits and Systems for Video Technology.

[35]  Xinpeng Zhang,et al.  Estimation of Image Rotation Angle Using Interpolation-Related Spectral Signatures With Application to Blind Detection of Image Forgery , 2010, IEEE Transactions on Information Forensics and Security.

[36]  Fan Yang,et al.  Keypoint-based copy-move detection scheme by adopting MSCRs and improved feature matching , 2017, Multimedia Tools and Applications.

[37]  Xiangyang Luo,et al.  Selection of Rich Model Steganalysis Features Based on Decision Rough Set $\alpha$ -Positive Region Reduction , 2019, IEEE Transactions on Circuits and Systems for Video Technology.

[38]  Wei Lu,et al.  Digital image splicing detection based on Markov features in DCT and DWT domain , 2012, Pattern Recognit..

[39]  Jiangqun Ni,et al.  Blind Forensics of Successive Geometric Transformations in Digital Images Using Spectral Method: Theory and Applications , 2017, IEEE Transactions on Image Processing.

[40]  Wei Lu,et al.  Robust image watermarking based on Tucker decomposition and Adaptive-Lattice Quantization Index Modulation , 2016, Signal Process. Image Commun..

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

[42]  Xinbo Gao,et al.  A learning-to-rank approach for image scaling factor estimation , 2016, Neurocomputing.

[43]  Rainer Böhme,et al.  The 'Dresden Image Database' for benchmarking digital image forensics , 2010, SAC '10.

[44]  Ingemar J. Cox,et al.  Normalized Energy Density-Based Forensic Detection of Resampled Images , 2012, IEEE Transactions on Multimedia.

[45]  Fei Xue,et al.  Natural image deblurring based on L0-regularization and kernel shape optimization , 2018, Multimedia Tools and Applications.

[46]  Jiangqun Ni,et al.  Effective Estimation of Image Rotation Angle Using Spectral Method , 2014, IEEE Signal Processing Letters.

[47]  Guoqiang Li,et al.  Digital image splicing detection based on Markov features in block DWT domain , 2018, Multimedia Tools and Applications.

[48]  Weiming Zhang,et al.  On the fault-tolerant performance for a class of robust image steganography , 2018, Signal Process..

[49]  Miroslav Goljan,et al.  Digital camera identification from sensor pattern noise , 2006, IEEE Transactions on Information Forensics and Security.