Benchmarking Robustness in Copy‐Move Forgery Detection: A Comparative Review of Algorithmic Trade‐Offs and Transformation Resilience

Copy‐move forgery detection (CMFD) remains a critical challenge in image forensics due to the increasing sophistication of post‐processing and geometric transformations. This paper presents a comprehensive benchmarking study and systematic review of CMFD research spanning the last decade (2015–2025). The study provides a qualitative synthesis of 91 empirical studies, categorized into four primary taxonomies: block‐based (36), keypoint‐based (27), deep learning‐based (17), and hybrid (11) methods. The study evaluates these approaches against realistic distortions, including JPEG compression, noise addition, blurring, and geometric operations such as rotation and scaling. The comparative analysis highlights significant trade‐offs among methodological categories: deep learning approaches demonstrate strong feature‐learning capabilities and robustness across diverse forgery scenarios, keypoint‐based methods offer a practical balance between computational efficiency and detection accuracy, while block‐based methods often achieve high localization precision at the expense of increased computational complexity. Furthermore, the performance of state‐of‐the‐art techniques is analyzed using F1‐measure results reported across widely used benchmark datasets. The review also discusses the challenges associated with cross‐study comparisons arising from variations in datasets, evaluation protocols, and reporting practices. Finally, this work identifies critical gaps in the field, including inconsistent benchmarking standards and dataset limitations, and outlines emerging research directions to support the development of more robust and generalizable image authentication systems.