UniMark: Artificial Intelligence Generated Content Identification Toolkit

• URL: http://arxiv.org/abs/2512.12324v1
• Date: Sat, 13 Dec 2025 13:30:48 GMT
• Title: UniMark: Artificial Intelligence Generated Content Identification Toolkit
• Authors: Meilin Li, Ji He, Jia Xu, Shanzhe Lei, Yan Teng, Yingchun Wang, Xuhong Wang,
• Abstract summary: We introduce the textbfUniMark, an open-source, unified framework for multimodal content governance.<n>Our system features a modular unified engine that abstracts complexities across text, image, audio, and video modalities.<n>This toolkit bridges the gap between advanced algorithms and engineering implementation, fostering a more transparent and secure digital ecosystem.
• Score: 16.336926299049825
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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