{"ID":2840947,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.12511","arxiv_id":"2511.12511","title":"DINO-Detect: A Simple yet Effective Framework for Blur-Robust AI-Generated Image Detection","abstract":"With growing concerns over image authenticity and digital safety, the field of AI-generated image (AIGI) detection has progressed rapidly. Yet, most AIGI detectors still struggle under real-world degradations, particularly motion blur, which frequently occurs in handheld photography, fast motion, and compressed video. Such blur distorts fine textures and suppresses high-frequency artifacts, causing severe performance drops in real-world settings. We address this limitation with a blur-robust AIGI detection framework based on teacher-student knowledge distillation. A high-capacity teacher (DINOv3), trained on clean (i.e., sharp) images, provides stable and semantically rich representations that serve as a reference for learning. By freezing the teacher to maintain its generalization ability, we distill its feature and logit responses from sharp images to a student trained on blurred counterparts, enabling the student to produce consistent representations under motion degradation. Extensive experiments benchmarks show that our method achieves state-of-the-art performance under both motion-blurred and clean conditions, demonstrating improved generalization and real-world applicability. Source codes will be released at: https://github.com/JiaLiangShen/Dino-Detect-for-blur-robust-AIGC-Detection.","short_abstract":"With growing concerns over image authenticity and digital safety, the field of AI-generated image (AIGI) detection has progressed rapidly. Yet, most AIGI detectors still struggle under real-world degradations, particularly motion blur, which frequently occurs in handheld photography, fast motion, and compressed video....","url_abs":"https://arxiv.org/abs/2511.12511","url_pdf":"https://arxiv.org/pdf/2511.12511v2","authors":"[\"Jialiang Shen\",\"Jiyang Zheng\",\"Yunqi Xue\",\"Huajie Chen\",\"Yu Yao\",\"Hui Kang\",\"Ruiqi Liu\",\"Helin Gong\",\"Yang Yang\",\"Dadong Wang\",\"Tongliang Liu\"]","published":"2025-11-16T08:54:00Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.LG\"]","methods":"[]","has_code":false,"code_links":[{"ID":607014,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2840947,"paper_url":"https://arxiv.org/abs/2511.12511","paper_title":"DINO-Detect: A Simple yet Effective Framework for Blur-Robust AI-Generated Image Detection","repo_url":"https://github.com/JiaLiangShen/Dino-Detect-for-blur-robust-AIGC-Detection","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}