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Double JPEG compression forensics based on a convolutional neural network
2016
EURASIP Journal on Information Security
Double JPEG compression detection has received considerable attention in blind image forensics. However, only few techniques can provide automatic localization. To address this challenge, this paper proposes a double JPEG compression detection algorithm based on a convolutional neural network (CNN). The CNN is designed to classify histograms of discrete cosine transform (DCT) coefficients, which differ between single-compressed areas (tampered areas) and double-compressed areas (untampered
doi:10.1186/s13635-016-0047-y
fatcat:k3tkxto23zbhfa6p2h2glddnga