Digital watermarking, a technique originally developed to protect intellectual property in traditional media, has emerged as a promising solution not only for identifying AI-generated content, but for protecting the actual intellectual property of AI models, as well. AI watermarking also serves broader purposes beyond copyright protection, including content authenticity verification, combating misinformation, and providing transparency about AI involvement in content creation.

Reliable watermarking could become a fundamental component of broader trust architecture for digital content, particularly as synthetic media becomes increasingly indistinguishable from human-created content . And, as AI continues to move to the edge, effective watermarking will become increasingly important for protecting intellectual property.

AI Content Attribution

Particularly important for protecting individuals from non-consensual synthetic media and reducing the spread of manipulated media, companies like TikTok have already implemented watermarking. Further, watermarking content attribution supports election integrity protection: with over 2 billion people voting globally in 2024, watermarking helped combat AI-generated political misinformation and maintain electoral integrity (FedScoop, 2024). In addition, watermarking helps educational institutions identify AI-generated submissions, supporting fair assessment and academic honesty (Forward Pathway, 2025).

Widespread adoption of watermarking could significantly enhance content attribution and verification, potentially reducing the spread of AI-generated misinformation by up to 40% in coming years (Schramowski et al., 2023). However, it will also require new frameworks for governance, transparency, and user consent.

Kirchenbauer et al. (2023) discuss several important implications of watermarking for the future of content attribution and verification. They suggest that “the watermarking method could be turned on only in certain contexts, for example when a specific user seems to exhibit suspicious behavior,” enabling more targeted application of content verification. Their discussion of multiple watermarks includes the possibility of “a company running a public/private watermarking scheme, giving the public access to one of the watermarks to provide transparency and independent verification that text was machine-generated,” while keeping a second watermark private for verification purposes.

AI Intellectual Property Protection

Training machine learning (ML) models is expensive in terms of computational power, amounts of labeled data, and human expertise. This investment needs protection, especially as model extraction attacks become more sophisticated and effective. As demonstrated by the work of Szyller et al. (2021), Chakraborty et al. (2022), and Luo et al. (2025), advanced watermarking techniques can provide robust protection for valuable AI models against theft and unauthorized use, supporting innovation and investment in AI development:

For Szyller et al. (2021), by making model extraction detectable and traceable, and enabling model owners to identify which client was responsible for extraction, watermarks create accountability that deters potential attackers from attempting extraction in the first place.

For Chakraborty et al. (2022), intellectual property protection is the primary goal for AI watermarking, particularly in the context of deep neural networks: “Extension of watermarking approaches to deep learning offers an effective solution to defend against model theft by allowing the owner to claim IP rights upon inspection of a suspected stolen model.” They explicitly state that their watermarking approach is designed as “an effective IP security solution against model extraction attacks on DL models deployed in edge devices.”

For Luo et al. (2025), digital watermarking technology “plays a crucial role in addressing security issues in the field of image generation and protecting the intellectual property rights of model owners.” They suggest that continued development of these technologies will help construct “a safer and more trustworthy ecosystem for AI-generated content” in the future.

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