Detecting Double Compressed JPEG Images by Using Moment Features of Mode Based DCT Histograms

The double compression of JPEG images is one of the important evidences of image tampering. The paper proposes a novel passive double compressed JPEG image detection algorithm using the moment features of the modes based DCT histogram´s characteristic function. Support vector machine is used as the classifier. Experimental results demonstrate that the proposed algorithm significantly increases the detection accuracy when the first compressing quality factor is large such as 95. In order to further improve the overall detection accuracy of double compressed JPEG in various quality factors, the paper proposes an improved algorithm by combing the moment features with the Mode Based Fist Digit features (MBFDF). The experimental results show that the overall detection accuracies can be further improved and the proposed algorithm outperforms some traditional methods, especially when the first compressing quality factor is large such as 95.