mlsanityData to 7 Oct 2026

Multi-axis Analysis of Image Manipulation Localization

arXiv 2605.20174 ↗First posted 19 May 2026cs.CV cs.LGcited by 0

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Abstract (arXiv)

Advanced image editing software enables easy creation of highly convincing image manipulations, which has been made even more accessible in recent years due to advances in generative AI. Manipulated images, while often harmless, could spread misinformation, create false narratives, and influence people's opinions on important issues. Despite this growing threat, there is limited research on detecting advanced manipulations across different visual domains. Thus, we introduce Analysis Under Domain-shifts, qualIty, Type, and Size (AUDITS), a comprehensive benchmark designed for studying axes of analysis in image manipulation detection. AUDITS comprises over 530K images from two distinct sources (user and news photos). We curate our dataset to support analysis across multiple axes using recent diffusion-based inpaintings, spanning a diverse range of manipulation types and sizes. We conduct experiments under different types of domain shift to evaluate robustness of existing image manipulation detection methods. Our goal is to drive further research in this area by offering new insights that would help develop more reliable and generalizable image manipulation detection methods.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 37 of its 49 references that are in the map. First posted on arXiv 19 May 2026.

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References · 37

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25 more
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