Venus-DeFakerOne: Unified Fake Image Detection & Localization
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 ...
| Authors | GuangJian Team |
|---|---|
| Primary category | cs.CV |
| Published | 13 May 2026 |
| Last updated at source | 3 June 2026 |
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