現実認識ギャップ：AI改変動画検出器は実世界に耐えられるか
Reality Perception Gap: Are AI-Manipulated Video Detectors Ready for the Real World?
SNS再圧縮や実機カメラ由来の劣化を含む1万本の動画ベンチマークRPGを構築し、18種の検出器が実運用条件で偽動画をほとんど検出できないことを示した。
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: Xiangrui Liu, Xinmiao Luan, Xinyuan Wang, Zhiyuan Fang, Chi-Yao Huang, Douglas C. Montgomery, Yanjie Fu, Felix Juefei-Xu, YeZhou Yang
分類: cs.CV
原文アブストラクト
AI-manipulated video detectors are typically evaluated on clean, curated datasets. In deployment, however, videos have usually passed through repost compression on social-media platforms and sometimes were captured by imperfect hardware. To measure detection under these conditions, we introduce Reality Perception Gap (RPG), a benchmark of 10,000 videos from frontier generators and partially synthetic edits, processed with cross-platform repost compression and paired with real footage that carries camera-side optical artifacts. RPG also includes socially fragile content, such as crime and wartime footage, where fabricated videos can cause severe social harm. Across 18 detectors, none catches more than 15% of fakes at a 1% false-positive rate. A positive control and a re-encoding sweep separate two sources of failure: generator and content shift degrades detectors before any compression, and repost compression erases the low-level traces that the artifact-based detectors relies on. Existing detectors therefore struggle when deployment conditions strip away the generation artifacts they rely on. These complementary failures suggest that robust detection requires training under deployment conditions and combining low-level, temporal, and semantic evidence, where passive detection may have reached its limits.