{"ID":2829554,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.12324","arxiv_id":"2512.12324","title":"UniMark: Artificial Intelligence Generated Content Identification Toolkit","abstract":"The rapid proliferation of Artificial Intelligence Generated Content has precipitated a crisis of trust and urgent regulatory demands. However, existing identification tools suffer from fragmentation and a lack of support for visible compliance marking. To address these gaps, we introduce the \\textbf{UniMark}, an open-source, unified framework for multimodal content governance. Our system features a modular unified engine that abstracts complexities across text, image, audio, and video modalities. Crucially, we propose a novel dual-operation strategy, natively supporting both \\emph{Hidden Watermarking} for copyright protection and \\emph{Visible Marking} for regulatory compliance. Furthermore, we establish a standardized evaluation framework with three specialized benchmarks (Image/Video/Audio-Bench) to ensure rigorous performance assessment. This toolkit bridges the gap between advanced algorithms and engineering implementation, fostering a more transparent and secure digital ecosystem.","short_abstract":"The rapid proliferation of Artificial Intelligence Generated Content has precipitated a crisis of trust and urgent regulatory demands. However, existing identification tools suffer from fragmentation and a lack of support for visible compliance marking. To address these gaps, we introduce the \\textbf{UniMark}, an open-...","url_abs":"https://arxiv.org/abs/2512.12324","url_pdf":"https://arxiv.org/pdf/2512.12324v3","authors":"[\"Meilin Li\",\"Ji He\",\"Yi Yu\",\"Jia Xu\",\"Shanzhe Lei\",\"Yan Teng\",\"Yingchun Wang\",\"Xuhong Wang\"]","published":"2025-12-13T13:30:48Z","proceeding":"cs.CR","tasks":"[\"cs.CR\",\"cs.AI\"]","methods":"[]","has_code":false}