With the development of powerful image processing tools and the increasing trend of using images as the main carrier of information, digital image forgery has become an increasingly serious issue. In copy-move forgery, one part of an image is copied and placed elsewhere in the same image. This paper puts forward an effective method based on SIFT for detecting copy-move forgery in digital image. The proposed method can accurately authenticate digital image and locate areas which have been tampered with. The algorithm starts by using scaleinvariant features transform (SIFT) to extract local image features, which are known as keypoints, and then searches for similar keypoints based on their Euclidean distances. Finally, the matched keypoints, which represent the copied and pasted areas, are associated with one and another to indicate which parts of the image have been tampered with. Experiments are performed to validate the effectiveness of this method on different attacks, and to quantify its robustness against post-processing. Results show that the method is robust against several geometric processings, including JPEG compression, rotation, noise, and scaling. As a representative result, when considering the standard test dataset MICC-F220, the proposed method achieves true and false positive rates of 100% and 3.12%, respectively.
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