Simple Domain Generalization for Strong Pixel-Level Image Tampering Detection in Modern VLMs
Modern vision-language models (VLMs) have significantly improved image generation and editing capabilities, making pixel-level image tampering detection increasingly important yet challenging under cross-model and out-of-distribution shifts. This work studies domain generalization for pixel-level image tampering detection in modern VLMs like ChatGPT, Gemini, Qwen-Image, etc., aiming to learn tampering localization models that remain robust across diverse VLM-generated manipulation distributions. We propose a simple yet effective domain-generalized training framework built on two practical stra
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- FuzzySimilar title/name (fuzzy) · 59%Tongyi-MAI/Z-Image-Turbo →
“Fuzzy title match (0.73): “Simple Domain Generalization for Strong Pixel-Level Image Ta” ≈ “Tongyi-MAI/Z-Image-Turbo””
- LinkedLinked via arxiv author · 85%Yi Tang →
“Simple Domain Generalization for Strong Pixel-Level Image Tampering Detection in Modern VLMs”
- LinkedLinked via arxiv author · 85%Xinyi Shang →
“Simple Domain Generalization for Strong Pixel-Level Image Tampering Detection in Modern VLMs”
- LinkedLinked via arxiv author · 85%Jiacheng Cui →
“Simple Domain Generalization for Strong Pixel-Level Image Tampering Detection in Modern VLMs”
- LinkedLinked via arxiv author · 85%Sondos Mahmoud Bsharat →
“Simple Domain Generalization for Strong Pixel-Level Image Tampering Detection in Modern VLMs”
- LinkedLinked via arxiv author · 85%Jiacheng Liu →
“Simple Domain Generalization for Strong Pixel-Level Image Tampering Detection in Modern VLMs”
- LinkedLinked via arxiv author · 85%Xiaohan Zhao →
“Simple Domain Generalization for Strong Pixel-Level Image Tampering Detection in Modern VLMs”
- LinkedLinked via arxiv author · 85%Tran Dinh Tien →
“Simple Domain Generalization for Strong Pixel-Level Image Tampering Detection in Modern VLMs”