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扩散已引发信任危机与紧迫的监管需求。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。为弥补这些不足，我们推出开源统一框架 **UniMark**，用于多模态内容治理。本系统采用模块化统一引擎，抽象化处理文本、图像、音频及视频模态的复杂性。关键创新在于提出新型双操作策略，原生支持用于版权保护的**隐式水印**与满足监管合规的**显式标记**。此外，我们建立了标准化评估框架，包含三个专项基准测试集（Image/Video/Audio-Bench），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，助力构建更透明、安全的数字生态系统。