IPAD-CLIP adapts CLIP via artifact-aware text embeddings to detect multi-class local perceptual artifacts, backed by a new dataset of 3520 images with pixel-level masks.
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ForensicsTok turns image manipulation localization into autoregressive token generation with a smoothing decoder and multi-scale forensic feature fusion, showing gains over MLLM baselines and slight gains over expert models on six benchmarks.
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IPAD-CLIP: Teaching CLIP to Detect Image Local Perceptual Artifacts
IPAD-CLIP adapts CLIP via artifact-aware text embeddings to detect multi-class local perceptual artifacts, backed by a new dataset of 3520 images with pixel-level masks.
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ForensicsTok: Forensics-Guided Tokenized Modeling for Image Tampering Localization
ForensicsTok turns image manipulation localization into autoregressive token generation with a smoothing decoder and multi-scale forensic feature fusion, showing gains over MLLM baselines and slight gains over expert models on six benchmarks.