Removal and injection of keypoints for SIFT-based copy-move counter-forensics

Recent studies exposed the weaknesses of scale-invariant feature transform (SIFT)-based analysis by removing keypoints without significantly deteriorating the visual quality of the counterfeited image. As a consequence, an attacker can leverage on such weaknesses to impair or directly bypass with alarming efficacy some applications that rely on SIFT. In this paper, we further investigate this topic by addressing the dual problem of keypoint removal, i.e., the injection of fake SIFT keypoints in an image whose authentic keypoints have been previously deleted. Our interest stemmed from the consideration that an image with too few keypoints is per se a clue of counterfeit, which can be used by the forensic analyst to reveal the removal attack. Therefore, we analyse five injection tools reducing the perceptibility of keypoint removal and compare them experimentally. The results are encouraging and show that injection is feasible without causing a successive detection at SIFT matching level. To demonstrate the practical effectiveness of our procedure, we apply the best performing tool to create a forensically undetectable copy-move forgery, whereby traces of keypoint removal are hidden by means of keypoint injection

Counter-forensics of SIFT-based copy-move detection by means of keypoint classification

Copy-move forgeries are very common image manipulations that are often carried out with malicious intents. Among the techniques devised by the 'Image Forensic' community, those relying on scale invariant feature transform (SIFT) features are the most effective ones. In this paper, we approach the copy-move scenario from the perspective of an attacker whose goal is to remove such features. The attacks conceived so far against SIFT-based forensic techniques implicitly assume that all SIFT keypoints have similar properties. On the contrary, we base our attacking strategy on the observation that it is possible to classify them in different typologies. Also, one may devise attacks tailored to each specific SIFT class, thus improving the performance in terms of removal rate and visual quality. To validate our ideas, we propose to use a SIFT classification scheme based on the gray scale histogram of the neighborhood of SIFT keypoints. Once the classification is performed, we then attack the different classes by means of class-specific methods. Our experiments lead to three interesting results: (1) there is a significant advantage in using SIFT classification, (2) the classification-based attack is robust against different SIFT implementations, and (3) we are able to impair a state-of-the-art SIFT-based copy-move detector in realistic cases

Secure and robust sift with resistance to chosen-plaintext attack

Scale-invariant feature transform (SIFT) is a powerful tool extensively used in the community of pattern recognition and computer vision. The security issue of SIFT, however, is relatively unexplored. We point out the potential weak-ness of SIFT, meaning that the SIFT features can be deleted or destroyed while maintaining acceptable visual qualities. To properly achieve the tradeoff between security and ro-bustness of SIFT, we present a cube-based secure transfor-mation mechanism to enable the SIFT method to resist up to the chosen plaintext attack while robustness against ge-ometric attacks can still be maintained. Security analysis and robustness verification are provided to demonstrate the effectiveness of the proposed (and modified) SIFT method