Splicing forgeries localization through the use of first digit features

Double JPEG compression forensics based on a convolutional neural network

“…To demonstrate the superiority of our method, we first compare the accuracy of double JPEG compression detection for different block sizes with the method proposed in [5], which detects double-compressed JPEG images by using mode-based first digit features, and the method proposed in [6], which uses the statistics of lowfrequency DCT coefficients. For composite JPEG image detection, we compare our localization results with the method proposed in [11], which achieves localization based on the DQ effects combined with Bayes's theorem, and also with the technique proposed in [12] based on Benford's law using SVM.…”

“…For double-compression detection in blind forensics, the histograms of doubly compressed images contain DQ Table 2 AUC values achieved on the UCID database by the proposed algorithm and the algorithms presented in [11] and [12] Proposed Method Reference Method [10] Reference Method [11] Fig. 9 AUC comparison of the proposed method, the method of [11], and the method of [12] on the UCID database Fig. 10 Detection results.…”

“…c The results of the method proposed in [11]. d The results of the method proposed in [12]. e The tampering masks effects which are quite different from the singly ones, and our proposed feature is the specified interval of these histograms.…”

“…Based on an improved statistical model, the method presented in [11] can detect either blockaligned or block-nonaligned compressed tampered regions. Amerini et al [12] localized the results of image splicing attacks based on the first digit features of DCT coefficients and employed a support vector machine (SVM) for classification. However, these methods function poorly when QF1 > QF2.…”

“…Finally, the tampered regions are located based on the classification results. The proposed technique is also compared with the schemes presented in [5,6,11] and the localization technique proposed in [12].…”