UniMark: Artificial Intelligence Generated Content Identification Toolkit
arXiv:2512.12324v3 Announce Type: replace 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 framewo
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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.
Comments: 5 Pages
Subjects:
Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
Cite as: arXiv:2512.12324 [cs.CR]
(or arXiv:2512.12324v3 [cs.CR] for this version)
https://doi.org/10.48550/arXiv.2512.12324
arXiv-issued DOI via DataCite
Submission history
From: Meilin Li [view email] [v1] Sat, 13 Dec 2025 13:30:48 UTC (9,289 KB) [v2] Fri, 26 Dec 2025 07:22:58 UTC (9,289 KB) [v3] Thu, 2 Apr 2026 13:11:10 UTC (9,286 KB)
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