SoK: Watermarking for AI-Generated Content

Watermarking is a practical tool for distinguishing AI from human-created content, embedding hidden signals that enable reliable detection as generative AI outputs become increasingly difficult to identify by sight or ear alone.

Watermarking alone cannot solve all GenAI risks, but the paper identifies it as playing "a crucial role in enhancing AI safety and trustworthiness by combating misinformation and deception"—suggesting it works best as part of a broader responsible AI strategy.

The field requires robust technical standards, as the paper formalizes definitions, desired properties, and threat models for watermarking schemes, indicating that effective implementation depends on schemes that can resist various attacks and manipulation attempts.

Regulatory and historical context matters for adoption, with the paper examining watermarking "from historical and regulatory perspectives," suggesting that policymakers need to understand both the technical capabilities and limitations when considering watermarking requirements for GenAI systems.

Significant open challenges remain in practical deployment, as the paper highlights the need for better evaluation strategies and identifies gaps in current approaches, meaning watermarking technology is still evolving and may not yet be a complete solution for all detection scenarios.