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.
@article{arxiv.2512.12324,
title = {UniMark: Artificial Intelligence Generated Content Identification Toolkit},
author = {Meilin Li and Ji He and Yi Yu and Jia Xu and Shanzhe Lei and Yan Teng and Yingchun Wang and Xuhong Wang},
journal= {arXiv preprint arXiv:2512.12324},
year = {2026}
}