Unified frequency and graph learning framework for accurate and robust copy-move image forgery detection.

Copy-move forgery (CMF) is one of the most prominent challenges to be addressed by digital image forensics experts today due to the ease with which forged objects can be copied and placed within the same image to conceal or replicate objects. The conventional approaches to detect CMF frequently suffer from a number of challenges, including increased computational complexity, poor localization accuracy, and reduced efficacy to detect minor forgeries. To mitigate the challenges associated with the conventional approach to detect copy-move forgeries, the current study aims to introduce a novel Dual-Phase Forgery Identification Network (DPFIN) that is capable of improving the efficacy of the detection process while exhibiting better resilience to complex forgeries. The proposed approach is based on a novel dual-phase preprocessing mechanism that combines the efficacy of frequency domain analysis with adaptive visual enhancement to detect hidden forgery artifacts. The proposed approach utilizes a YOLOv5-based backbone to extract multi-scale features for the input images. Furthermore, a learning mechanism based on the Zebra Optimization Algorithm is also introduced to ensure stable training and better generalization performance for the model. The performance evaluation of the proposed DPFIN is carried out to achieve 99.89% accuracy and 98.89% F1-score with a false positive rate of 0.21% on the CoMoFoD dataset. Also, 99.99% AUC is achieved with 96.8% F1-score on the CASIA v2 dataset. This proves that the proposed DPFIN is a reliable solution for image forgery detection.