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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 …