APFL: Active-Passive Forgery Localization for Medical Images

Nan Wang, Jiaqi Shi, Liping Yi, Gang Wang, Ming Su, Xiaoguang Liu

Abstract: Medical image forgery has become an urgent issue in academia and medicine. Unlike natural images, images in the medical field are so sensitive that even minor manipulation can produce severe consequences. Existing forgery localization methods often rely on a single image attribute and suffer from poor generalizability and low accuracy. To this end, we propose a novel active-passive forgery localization (APFL) algorithm to locate the forgery region of medical images attacked by three common forgeries: splicing, copy-move and removal. It involves two modules: a) active forgery localization, we utilize reversible watermarking to locate the fuzzy forgery region, and b) passive forgery localization, we train a lightweight model named KDU-Net through knowledge distillation to precisely locate the forgery region in the fuzzy localization result extracted by active forgery localization. The lightweight KDU-Net as a student model can achieve similar performance to RRU-Net as a teacher model, while its model capacity is only \( 24.6\%\) of RRU-Net, which facilitates fast inference for medical diagnostic devices with limited computational power. Since there are no publicly available medical tampered datasets, we manually produce tampered medical images from the real-world Ophthalmic Image Analysis (OIA) fundus image dataset. The experimental results present that APFL achieves satisfied forgery localization accuracy under the three common forgeries and shows robustness to rotation and scaling post-processing attacks.