Copy-Move Forgery Detection Using Segment Anything Model and BRISK Feature

In today's age., the image manipulation techniques make it more challenging to verify the authenticity of digital content. Copy-move forgery., where objects of an image are copied and pasted within the same image., is a common type of manipulation that generally used. This research presents an approach for copy-move forgery detection (CMFD). It combines the Segment Anything Model (SAM) with the Binary Robust Invariant Scalable Keypoints (BRISK) feature extraction technique. It applies the SAM's segmentation ability to provide a robust foundation for detecting image components., and BRISK efficiency and reliability for keypoint detection and description. The results implies that the method has a well-balanced detection capability and effectively managing the trade-off between false positives and false negatives. It shown in the performance metrics with accuracy of 92 %., precision of 91 %., recall of 93%., and f1 score of 92%. The integration of SAM and BRISK enhances the robustness of the CMFD and makes it adaptable to difference image types and resilient to common image transformations like rotating and scaling the objects shown by TPR of 87.5% for the rotation of 30 degrees transformation., and 97.4% for the rotation of 30 degrees and scaled for 120%.