A Targeted Counter-Forensics Method for SIFT-Based Copy-Move Forgery Detection
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The Scale Invariant Feature Transform (SIFT) has been widely used in a lot of applications for image feature matching. Such atransform allows us to strong matching ability, stability in rotation, and scaling with the variety of different scales. Recently, it has beenmade one of the most successful algorithms in the research areas of copy-move forgery detections. Though this transform is capable ofidentifying copy-move forgery, it does not widely address the possibility that counter-forensics operations may be designed and used tohide the evidence of image tampering. In this paper, we propose a targeted counter-forensics method for impeding SIFT-based copy-moveforgery detection by applying a semantically admissible distortion in the processing tool. The proposed method allows the attacker todelude a similarity matching process and conceal the traces left by a modification of SIFT keypoints, while maintaining a high fidelitybetween the processed images and original ones under the semantic constraints. The efficiency of the proposed method is supported byseveral experiments on the test images with various parameter settings.
Abstract
The Scale Invariant Feature Transform (SIFT) has been widely used in a lot of applications for image feature matching. Such atransform allows us to strong matching ability, stability in rotation, and scaling with the variety of different scales. Recently, it has beenmade one of the most successful algorithms in the research areas of copy-move forgery detections. Though this transform is capable ofidentifying copy-move forgery, it does not widely address the possibility that counter-forensics operations may be designed and used tohide the evidence of image tampering. In this paper, we propose a targeted counter-forensics method for impeding SIFT-based copy-moveforgery detection by applying a semantically admissible distortion in the processing tool. The proposed method allows the attacker todelude a similarity matching process and conceal the traces left by a modification of SIFT keypoints, while maintaining a high fidelitybetween the processed images and original ones under the semantic constraints. The efficiency of the proposed method is supported byseveral experiments on the test images with various parameter settings.
참고문헌 (17)
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