Robust median filtering detection based on the difference of frequency residuals

Recently, the detection of median filtering (MF), which is a popular nonlinear denoising manipulation, has attracted extensive attention from researchers. Several detectors with satisfying performance have been developed, while most of them need to train proper classifiers and their performance may be degraded under JPEG compression. In this paper, a training-free MF detector with single-dimensional feature is proposed based on the difference of frequency residuals, which can solve the detection issue of median filtering images under JPEG post-processing. It is designed relying on the fact that when an image is median filtered over and over again, the frequency residual obtained from continuous two images monotonically decreases. The difference between the frequency residuals obtained from the first MF and the second MF is pretty large in an unfiltered test image, while it is relatively small if the test image is a median filtered one. Thus, the unfiltered and the median filtered images are distinguishable. Furthermore, a novel strategy combining unsharp masking (USM) sharpening is implemented to suppress the effect of image content and find a universal threshold which is utilized to classify two types of images. Experimental results show that the proposed method outperforms some state-of-the-art methods at the condition of a low false alarm rate, especially when the test images are in low quality and low resolution.

[1]  K. J. Ray Liu,et al.  Robust Median Filtering Forensics Using an Autoregressive Model , 2013, IEEE Transactions on Information Forensics and Security.

[2]  Alessandro Piva,et al.  Image Forgery Localization via Fine-Grained Analysis of CFA Artifacts , 2012, IEEE Transactions on Information Forensics and Security.

[3]  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.

[4]  Tomás Pevný,et al.  Steganalysis by subtractive pixel adjacency matrix , 2010, IEEE Trans. Inf. Forensics Secur..

[5]  Tomás Pevný,et al.  Towards dependable steganalysis , 2015, Electronic Imaging.

[6]  Rongrong Ni,et al.  Forensic identification of resampling operators: A semi non-intrusive approach. , 2012, Forensic science international.

[7]  Yao Zhao,et al.  Robust median filtering detection based on local difference descriptor , 2017, Signal Process. Image Commun..

[8]  Yun Q. Shi,et al.  Revealing the Traces of Median Filtering Using High-Order Local Ternary Patterns , 2014, IEEE Signal Processing Letters.

[9]  Yao Zhao,et al.  Median filtering detection of small-size image based on CNN , 2018, J. Vis. Commun. Image Represent..

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

[11]  Yao Zhao,et al.  Contrast Enhancement-Based Forensics in Digital Images , 2014, IEEE Transactions on Information Forensics and Security.

[12]  Gerald Schaefer,et al.  UCID: an uncompressed color image database , 2003, IS&T/SPIE Electronic Imaging.

[13]  Jiwu Huang,et al.  Blind Detection of Median Filtering in Digital Images: A Difference Domain Based Approach , 2013, IEEE Transactions on Image Processing.

[14]  Hai-Dong Yuan,et al.  Blind Forensics of Median Filtering in Digital Images , 2011, IEEE Transactions on Information Forensics and Security.

[15]  Jessica J. Fridrich,et al.  On detection of median filtering in digital images , 2010, Electronic Imaging.

[16]  Rafael C. González,et al.  Local Determination of a Moving Contrast Edge , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[18]  Yao Zhao,et al.  Forensic detection of median filtering in digital images , 2010, 2010 IEEE International Conference on Multimedia and Expo.

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

[20]  Lingying Chen,et al.  Block Sampled Matching with Region Growing for Detecting Copy-Move Forgery Duplicated Regions , 2017, J. Inf. Hiding Multim. Signal Process..

[21]  Alan C. Bovik,et al.  Streaking in median filtered images , 1987, IEEE Trans. Acoust. Speech Signal Process..