Digital image forensics, particularly in the realm of detecting Copy-Move Forgery (CMF), is exposed to significant challenges, especially in the face of intricate adversarial attacks. In response to these challenges, this paper presents a robust approach for detecting complex CMFs in digital images using the KeyPoint-Siamese Capsule Network (KP-SCN) and evaluates its resilience against adversarial attacks. The KP-SCN architecture incorporates keypoint detection, a Siamese network for feature extraction, and a capsule network for forgery detection. The method showcases enhanced robustness against adversarial attacks, specifically addressing image perturbation, patch removal, patch replacement, and spatial transformation attacks. By using hierarchical feature representations and dynamic routing in capsule networks, the model effectively handles complex CMF, including rotation, scaling, and non-linear transformations. The proposed KP-SCN approach employs a large dataset for training the KP-SCN, enabling it to identify copy-move forgeries by comparing extracted keypoints and their spatial relationships. KP-SCN demonstrates superior performance compared to the state-of-the-art on the CoMoFoD dataset, achieving precision, recall, and F1-score values of 95.62%, 93.78%, and 94.69%, respectively, and shows strong results on other datasets. For CASIA v2.0, the precision, recall, and F1-score are 90.45%, 88.97%, and 89.70%; for MICC-F2000, they are 91.32%, 90.27%, and 90.79%; for MICC-F600, they are 92.21%, 91.10%, and 91.65%; for MICC-F8multi, they are 89.75%, 87.92%, and 88.83%; and for IMD, they are 93.14%, 92.58%, and 92.86%. The KP-SCN framework maintains high detection rates under various manipulations, including JPEG compression, rotation, scaling, noise, blurring, brightness changes, contrast adjustment, and zoom motion blur compared to the other methods. For instance, it achieves an 80.657% detection rate for CoMoFoD under JPEG compression and 97.883% for IMD under a 10-degree rotation. These findings validate the robustness and adaptability of KP-SCN, making it a reliable solution for real-world forensic applications.
