Brief

Researchers have introduced Impostor, a new benchmark dataset designed to evaluate the localization of AI-generated image manipulations. This dataset comprises 100,000 manipulated images created using a closed-loop agent framework called CraftAgent, which automates the generation of diverse and realistic edits. Impostor covers manipulations from seven recent AIGC models and includes multiple edited regions, presenting a significant challenge for current image manipulation detection methods and large vision-language models. The paper also proposes PhaseAware-Net (PANet), a novel framework that improves localization accuracy by incorporating local phase modeling and semantic-forensic consistency. AI