Copy-Move Image Forgery Detection and Classification Using Sift and Birch Approaches

Copy-move forgery detection (CMFD) continues to pose significant challenges in digital image forensics, particularly under geometric transformations such as rotation and scaling. This study presents a detection pipeline that couples Scale-Invariant Feature Transform (SIFT) keypoints with the Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) algorithm. Leveraging BIRCH's robustness to noise, computational efficiency and relatively mild hyper-parameter sensitivity, the proposed framework effectively suppresses spurious correspondences and isolates candidate tampered regions with high confidence. Performance is evaluated on the MICC-F220 benchmark and benchmarked against two alternative clustering strategies-Hierarchical Agglomerative Clustering (HAC) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN)-each configured with their best-reported parameter sets. Under the tested settings, the SIFT + BIRCH scheme attains a true-positive rate of 99 % while sustaining a low false-positive rate, outperforming both baselines. The results underscore the suitability of hierarchical clustering for spatially grouping SIFT keypoints and highlight BIRCH as a promising direction for future CMFD research, particularly when integrated with complementary outlier-rejection mechanisms.