MATLAB Implementation of Image Copy-Move Forgery Detection Using SURF Features

This implementation uses MATLAB to detect copy-move forgeries in digital images through SURF-based feature analysis. The methodology involves extracting distinctive SURF features from images and comparing them to identify potential tampering. Specifically, the algorithm employs the SURF feature detector to identify keypoints and compute their corresponding descriptors within the image. The implementation includes calculating similarity metrics between these feature descriptors using appropriate distance measures. When the similarity between different regions exceeds a predefined threshold, the system flags the image as containing copy-move forgery. The code utilizes MATLAB's Computer Vision Toolbox functions such as detectSURFFeatures() for keypoint detection and extractFeatures() for descriptor computation. Matching is typically performed using k-nearest neighbor search with distance ratio testing to minimize false positives. This approach has significant applications in digital image forensics and copyright protection, providing an effective method for authenticating image integrity and detecting malicious alterations. The implementation handles various image formats and includes parameter tuning options for optimal performance across different image types and tampering scenarios.