Digital images are one of the primary modern media for information interchange. However, digital images are vulnerable to interception and manipulation due to the wide availability of image editing software tools. Filtering forgery detection and splicing detection are two of the most important problems in digital image forensics. In particular, the primary challenge for the filtering forgery detection problem is that typically the techniques effective for nonlinear filtering (e.g. median filtering) detection are quite ineffective for linear filtering detection, and vice versa. In this paper, we have used Discrete Cosine Transform Residual features to train a Support Vector Machine classifier, and have demonstrated its effectiveness for both linear and non-linear filtering (specifically, Median Filtering) detection and filter classification, as well as re-compression based splicing detection in JPEG images. We have also theoretically justified the choice of the abovementioned feature set for both type of forgeries. Our technique outperforms the state-of-the-art forensic techniques for filtering detection, filter classification and re-compression based splicing detection, when applied on a set of standard benchmark images.
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