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.

翻译：人工智能生成内容的快速激增引发了信任危机和迫切的监管需求。然而，现有识别工具存在碎片化问题，且缺乏对可见合规标记的支持。为应对这些挑战，我们提出了UniMark，一个开源、统一的多模态内容治理框架。该系统采用模块化的统一引擎，抽象了文本、图像、音频和视频模态的复杂性。更重要的是，我们提出了一种新颖的双操作策略，原生支持用于版权保护的《隐藏水印》和用于监管合规的《可见标记》。此外，我们构建了一个标准化的评估框架，包含三个专用基准（图像/视频/音频基准），以确保严格的性能评估。该工具包弥合了先进算法与工程实现之间的鸿沟，促进了更透明、更安全的数字生态系统。