DUNK: Dual-Space Non-Semantic Knowledge Consolidation for Continual Image Forgery Localization

Image Forgery Localization (IFL) aims to locate manipulated regions at the pixel level. However, with the rapid evolution of generative artificial intelligence and image editing tools, newly emerging manipulation patterns continuously cause severe distribution shifts, making existing static IFL methods suffer from catastrophic forgetting when adapted to streaming forgery data. Therefore, in this paper, we focus on a challenging practical task called Continual Image Forgery Localization (CIFL), which requires continuously adapting to new manipulation patterns while preserving fine-grained localization ability for historical ones. To this end, we propose a Dual-Space non-semantic Knowledge Consolidation (DUNK) framework to maintain and consolidate historical authentic-forgery discrepancy knowledge in both spatial and frequency spaces. Specifically, a Dual-space authentic-forgery Prototype Memory (DPM) module is designed to compactly preserve structured authentic and forged prototypes without storing massive raw samples. Then, a Dual-space Adaptive Prototype Replay (DAPR) module adaptively coordinates spatial and frequency prototype replay according to their task-specific reliability, thereby preserving complementary historical forgery cues. Finally, a Multi-Perspective Knowledge Distillation (MPKD) module constrains the consistency between old and new models from mask, boundary, and feature perspectives to alleviate the degradation of fine-grained localization ability. Extensive experiments on multiple forgery localization benchmarks demonstrate the superiority of our method against existing IFL and continual learning methods in both localization accuracy and anti-forgetting ability. Our code will be released soon.