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The forensic investigation of JPEG compression generally relies on the analysis of first-order statistics based on image histogram. The JPEG compression detection methods based on such methodology can be effortlessly circumvented by adopting some anti-forensic attacks. This paper presents a counter JPEG anti-forensic method by considering the second-order statistical analysis based on the co-occurrence matrices (CMs). The proposed framework comprises three stages: selection of the target difference image, evaluation of CMs, and generation of second-order statistical feature based on CMs. In the first stage, we explore the effects of dithering operation of JPEG anti-forensics by analyzing the variance inconsistencies along the diagonals. Afterward, CMs are evaluated in the second stage to highlight the effects of grainy noise introduced during the dithering operation. The third stage is devoted to generate an optimal second-order statistical feature which is fed to the SVM classifier. The experimental results based on the uncompressed color image database and BOSSBase dataset images demonstrated that the proposed forensic detector based on CM is very efficient even in the presence of anti-forensic attacks. Moreover, the experimental results also confirm the competency of the proposed method in counter median filtering and contrast enhancement anti-forensics. The proposed scheme also provides satisfactory results in detecting other image processing operations such as mean filtering, Gaussian filtering, Weiner filtering, scaling, and rotation, thereby revealing its multi-purpose nature.
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