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SoK: On the Role and Future of AIGC Watermarking in the Era of Gen-AI

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arxiv 2411.11478 v2 pith:KLR2P3ZL submitted 2024-11-18 cs.CR

classification cs.CR
keywords aigcwatermarkingdifferentexistingfuturepropertiessurveystaxonomy
verification ladder T0 review T1 audit T2 compute T3 formal
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The rapid advancement of AI technology, particularly in generating AI-generated content (AIGC), has transformed numerous fields, e.g., art video generation, but also brings new risks, including the misuse of AI for misinformation and intellectual property theft. To address these concerns, AIGC watermarks offer an effective solution to mitigate malicious activities. However, existing watermarking surveys focus more on traditional watermarks, overlooking AIGC-specific challenges. In this work, we propose a systematic investigation into AIGC watermarking and provide the first formal definition of AIGC watermarking. Different from previous surveys, we provide a taxonomy based on the core properties of the watermark which are summarized through comprehensive literature from various AIGC modalities. Derived from the properties, we discuss the functionality and security threats of AIGC watermarking. In the end, we thoroughly investigate the AIGC governance of different countries and practitioners. We believe this taxonomy better aligns with the practical demands for watermarking in the era of GenAI, thus providing a clearer summary of existing work and uncovering potential future research directions for the community.

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Cited by 2 Pith papers

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  1. Rethinking Data Protection in the (Generative) Artificial Intelligence Era

    cs.LG 2025-07 conditional novelty 5.0 of 10

    The paper organizes data protection in generative AI into a four-level hierarchy covering non-usability, privacy preservation, traceability, and deletability, and maps techniques and regulations onto it.

  2. First-Place Solution to NeurIPS 2024 Invisible Watermark Removal Challenge

    cs.CV 2025-08 conditional novelty 4.0 of 10

    A competition-winning pipeline removes 95.7% of StegaStamp and TreeRing watermarks on the NeurIPS 2024 benchmark by combining VAE fine-tuning, diffusion purification, and translation tricks.

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