Analytical Global Median Filtering Forensics Based on Moment Histograms
Abhinav Kumar Gupta, Divya Singhal · ACM Transactions on Multimedia Computing Communications and Applications · 2018
Median filtering forensics in images has gained wide attention from researchers in recent years because of its inherent nature of preserving visual traces. Although many forensic methods are developed for median filtering detection, probability of detection reduces under JPEG compression at low-quality factors and for low-resolution images. The feature set reduction is also a challenging issue among existing detectors. In this article, a 19-dimensional feature set is analytically derived from image skewness and kurtosis histograms. This new feature set is exploited for the purpose of global median filtering forensics and verified with exhaustive experimental results. The efficacy of the method is tested on six popular databases (UCID, BOWS2, BOSSBase, NRCS, RAISE, and DID) and found that the new feature set uncovers filtering traces for moderate, low JPEG post-compression and low-resolution operation. Our proposed method yields lowest probability of error and largest area under the ROC curve for most of the test cases in comparison with previous approaches. Some novel test cases are introduced to thoroughly assess the benefits and limitations of the proposed method. The obtained results indicate that the proposed method would provide an important tool to the field of passive image forensics.