Exposing seam carving forgery under recompression attacks by hybrid large feature mining (original) (raw)

While seam carving has been widely used in computer vision and multimedia processing, it is also used for tampering illusions. Although several methods have been proposed to detect seam carving-based forgery, to this date, the detection of the seam carving forgery under recompression attacks in JPEG images has not been explored. To fill this gap, we proposed a hybrid large scale feature mining-based detection method to distinguish the doctored JPEG images from the untouched JPEG images under recompression attacks. Over one hundred thousand features from the spatial domain and from the DCT transform domain are extracted. Ensemble learning is used to deal with the high dimensionality and to avoid overfitting that may occur with some traditional learning classifier for the detection. Our study demonstrates the efficacy of proposed approach to exposing the seam-carving forgery under recompression attacks, especially from a lower quality level or on the same quality recompression.