UniMark: Artificial Intelligence Generated Content Identification Toolkit
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Abstract (arXiv)
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 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 Hidden Watermarking for copyright protection and 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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 14 references that are in the map. First posted on arXiv 13 Dec 2025.
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10 of 14 references.
References · 10
- 2025Qwen2.5-VL Technical Reportcited by 5,587
- 2025Wan: Open and Advanced Large-Scale Video Generative Modelscited by 2,708
- 2023The Stable Signature: Rooting Watermarks in Latent Diffusion Modelscited by 304
- 2024An Undetectable Watermark for Generative Image Modelscited by 81
- 2024Proactive Detection of Voice Cloning with Localized Watermarkingcited by 78
- 2024Video Seal: Open and Efficient Video Watermarkingcited by 48
- 2024GumbelSoft: Diversified Language Model Watermarking via the GumbelMax-trickcited by 37
- 2024Attack-Resilient Image Watermarking Using Stable Diffusioncited by 35
- 2024GROOT: Generating Robust Watermark for Diffusion-Model-Based Audio Synthesiscited by 21
- 2024CredID: Credible Multi-Bit Watermark for Large Language Models Identificationcited by 4
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- 2025Watermarking Without Standards Is Not AI Governance
- 2026From Forensics to Ecosystems: Rethinking Watermarks for Generative AI Oversight
- 2026Who Gets Flagged? The Pluralistic Evaluation Gap in AI Content Watermarking
- 2025Watermarking Large Language Models in Europe: Interpreting the AI Act in Light of Technology
- 2025On-Device Watermarking: A Socio-Technical Imperative For Authenticity In The Age of Generative AI
- 2025A Practical Synthesis of Detecting AI-Generated Textual, Visual, and Audio Content
- 2026Beyond AI-Generated Labels: Watermarking, Co-Creation, and Conflation of AI-Generation with Disinformation
- 2026Toward Accountable AI-Generated Content on Social Platforms: Steganographic Attribution and Multimodal Harm Detection