GAP-SAM: グローバルアーティファクト事前情報による汎用的なAI生成画像改ざん位置特定
GAP-SAM: A Global Artifact Prior for Generalizable AI-Generated Image Manipulation Localization
AI生成画像の改ざん位置を特定する手法で、画像とそのVAE再構成からグローバルなアーティファクト情報を抽出し、SAM3の特徴ピラミッドに注入することで、セマンティック境界への過学習を抑えつつ高い汎化性能を実現した。
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著者: Haozhen Yan, Siyuan Shan, Zijian Yu, Youqi Wang, Yan Hong, Jun Lan, Jianfu Zhang
分類: cs.CV
原文アブストラクト
AI-generated image manipulation localization identifies edited pixels, but its OOD performance lags behind image-level detection partly because pixel supervision entangles forensic evidence with dataset-specific mask geometry and semantic boundaries. Extending image-level distribution alignment to localization, we construct COCO-ControlNet with source-image Canny edges and depth maps to align semantics and geometry, improving OOD performance across multiple localizers. Yet tighter Mask-VAE Reconstruction Alignment (Mask-VAE) underperforms COCO-ControlNet, showing that VAE reconstruction artifacts transfer poorly to local diffusion-inpainting artifacts. We also identify \emph{boundary adhesion}, where fine-tuned segmentation models snap predictions to semantic object contours rather than true manipulation boundaries. These findings motivate GAP-SAM, which encodes an image and its frozen VAE reconstruction into a global artifact token and injects it into SAM3's feature pyramid via zero-gated FiLM before pixel decoding. Without prescribing a spatial region, this token modulates dense decoding to preserve localization while suppressing semantic-boundary shortcuts. Across six datasets, GAP-SAM averages 79.8 Pixel-F1, outperforming the strongest prior method by 12.6 points. It also performs best at every tested severity of JPEG compression, Gaussian blur, and resizing.
