UniMark: Artificial Intelligence Generated Content Identification Toolkit
This addresses the trust and regulatory crisis in digital content for users and regulators, though it appears incremental as it builds on existing identification methods with added compliance features.
The authors tackled the problem of fragmented and non-compliant identification tools for AI-generated content by introducing UniMark, an open-source unified framework for multimodal content governance, which achieved support for both hidden watermarking and visible marking across text, image, audio, and video modalities.
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.