Median filtering forensics using spatial and frequency domain residuals

Yakun Niu, Xiangru Chen, Hongjian Yin

Abstract: As one of the most important topics in image forensics, median filtering detection has developed rapidly in recent years. However, the robustness to JPEG compression is still challenging, especially for small image blocks and low quality compression. We find that when an image is undergone successively median filtering and JPEG compression operations, the median filtering residual (MFR) between the sequential two versions tends to converge. However, the convergence rate for the median filtered image is pretty faster than that for the original one. Based on this, in this paper, we present a JPEG image median filtering forensic method using both spatial and frequency domain residuals. To measure the convergence rate, the nonzero coefficients together with autoregressive coefficients of multiple MFRs are extracted in the spatial domain. Furthermore, a calibration strategy based on image sharpening is proposed in the frequency domain for capturing the convergence difference of MFRs between unaltered and median filtered images. Finally, the complementary features extracted in the two domains are concatenated for the detection task. Experimental results demonstrate the proposed approach is able to accurately detect median filtering and outperforms some state-of-the-art methods, especially in the scenario of small image blocks.