画像改ざん位置特定の多軸解析
Multi-axis Analysis of Image Manipulation Localization
画像編集技術の進歩に伴い、高度な改ざん画像の検出が課題となっている。本論文では、異なる視覚領域での改ざん検出の堅牢性を評価するための包括的ベンチマーク「AUDITS」を紹介し、拡散モデルを用いた多様な改ざん画像を含む53万枚以上のデータセットを構築した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Keanu Nichols, Divya Appapogu, Giscard Biamby, Dina Bashkirova, Anna Rohrbach, Bryan A. Plummer
分類: cs.CV, cs.LG
原文アブストラクト
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.
