Forgery Detection and Tampering Localization of Double JPEG Compression Based on First Digit Features of DCT Coefficients and KNR

A337 improved forgery detection and tampering localization method is proposed for double JPEG compression images based on first digit feature of discrete cosine transform (DCT) coefficients and kernel-based nonlinear representor (KNR). First, a to-be-checked JPEG image is divided into overlapping blocks of size $64\times 64$ , and the first digit (1∼9) features of alternating current (AC) DCT coefficients at the first nine positions of every $8\times 8$ blocks are obtained in each image block, followed by principal component analysis (PCA) transform to form compact features. And then the KNR classifier is used to judge whether the corresponding image block has been re-compressed. Finally, the test results of double compression are used to locate the tampered area of JPEG image. Experimental results show that in comparison with representative algorithms, the improved algorithm achieves better results, and is robust to operations such as rotation, resizing and feathering. Moreover, KNR classifier outperforms classical SVM classifier in recognition effect and efficiency.