Venus-DeFakerOne: 統合型偽造画像検出・位置特定
Venus-DeFakerOne: Unified Fake Image Detection & Localization
生成AIの進化に伴い多様化する画像偽造に対し、既存の検出手法は領域ごとに分断されている。本論文では、InternVL2とSAM2を統合した統一基盤モデルDeFakerOneを提案し、画像レベルの検出と画素レベルの位置特定を同時に実現する。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: GuangJian Team
分類: cs.CV
原文アブストラクト
In recent years, the rapid evolution of generative AI has fundamentally reshaped the paradigm of image forgery, breaking the traditional boundaries between document editing, natural image manipulation, DeepFake generation, and full-image AIGC synthesis. Despite this shift toward unified forgery generation, existing research in Fake Image Detection and Localization (FIDL) remains fragmented. This creates a mismatch between increasingly unified forgery generation mechanisms and the domain-specific detection paradigm. Bridging this mismatch poses two key challenges for FIDL: understanding cross-domain artifacts transfer and interference, and building a high-capacity unified foundation model for joint detection and localization. To address these challenges, we propose DeFakerOne, a data-centric, unified FIDL foundation model integrating InternVL2 and SAM2. DeFakerOne enables simultaneous image-level detection and pixel-level forgery localization across diverse scenarios. Extensive experiments demonstrate that DeFakerOne achieves state-of-the-art performance, outperforming baselines on 39 forgery detection benchmarks and 9 localization benchmarks. Furthermore, the model exhibits superior robustness against real-world perturbations and state-of-the-art generators such as GPT-Image-2. Finally, we provide a systematic analysis of data scaling laws, cross-domain artifacts transfer-interference patterns, the necessity of fine-grained supervision, and the original resolution artifacts preservation, highlighting the design principles for scalable, robust, and unified FIDL.
