Existing algorithms for detecting image copy-paste forgery can identify pairwise-similar regions in suspicious images but cannot accurately locate tampered regions. To address such drawback

we proposed a novel method for the automatic detection and localization of tampered regions on the basis of the offset estimation of double JPEG(Joint Photographic Experts Group)image compression. First

key points and their corresponding feature vectors were extracted using the SIFT(scale invariant features tromsform) algorithm

and a preliminary match of the feature vectors was implemented with the nearest neighbor algorithm. Second

the mismatch caused by inconsistent color information was eliminated by combining the preliminary matching points with the HSI(hue saturation intensity) color features to optimize the initial matching key points. Third

the matched key points were used to estimate the affine transform parameters and to eliminate the mismatching points with the RANSAC(random sample consensus) algorithm. Fourth

all the pixels within the copy-move regions were obtained by building a region correlation map. Finally

the duplicated region and the forgery region were distinguished from the copy-move regions according to the estimated offset of the double JPEG image compression. Compared with the classical SIFT and SURF(speeded up robust features) detection methods

our method can achieve higher true positive rates and effectively reduce false positive rates. When the second JPEG compression quality factor is greater than the first

the rate of forgery region detection can reach more than 96%. The proposed approach can effectively locate the forgery regions of JPEG images counterfeited via copy-paste tampering. It also demonstrates excellent robustness for copied regions distorted by geometric transformations and common post-processing operations.