A JPEG image is double-compressed if it underwent JPEG compression twice, each time with a different quantization matrix but with the same 8 × 8 grid. Some popular steganographic algorithms (Jsteg, F5, OutGuess) naturally produce such double-compressed stego images. Because double-compression may significantly change the statistics of DCT coefficients, it negatively influences the accuracy of some steganalysis methods developed under the assumption that the stego image was only single-compressed. This paper presents methods for detection of double-compression in JPEGs and for estimation of the primary quantization matrix, which is lost during recompression. The proposed methods are essential for construction of accurate targeted and blind steganalysis methods for JPEG images, especially those based on calibration. Both methods rely on support vector machine classifiers with feature vectors formed by histograms of low-frequency DCT coefficients. 1
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