Forensic-Aware Continual Adaptation for Image Forgery Localization
The work addresses how Image Forgery Localization (IFL) models fail when faced with new types of image manipulations over time. It proposes a continual learning framework using a Spatial Mixture-of-Forensic-Experts module to adapt to new data and Fisher-weighted LoRA Gradient surgery to keep old knowledge. This allows IFL models to learn from new forgery styles while preventing them from forgetting what they already learned.