Anthropic Rolls Out Invisible Statistical Watermarks for Claude Models to Comply with EU AI Act
On 2 August 2026, the same day Article 50 of the EU AI Act entered into force, Anthropic began embedding an invisible statistical watermark in every text generated by its latest Claude models, including Opus 5 and Sonnet 5. The company presented the measure as compliance with the voluntary Code of Practice on AI content transparency endorsed by the European Commission.
The watermarking system is two-layered. For text, a secret key known only to Anthropic partitions the model’s candidate tokens at each step into “green” and “red” groups and slightly increases the probability of selecting from the green group. Over hundreds of tokens this creates a detectable statistical bias that cannot be reproduced by chance without knowledge of the key. For image and document files the system uses signed C2PA metadata, the same open standard already used in photojournalism.
Although the regulation applies only to content served to European users, Anthropic chose to activate the watermark globally. Maintaining separate model versions for different jurisdictions would have imposed significant engineering overhead, so a single worldwide deployment was implemented.
Within 24 hours of the announcement, GitHub projects such as watermarks-remover by Guillaume Meyer appeared, quickly attracting thousands of stars. Commercial services including StealthGPT, claudewatermark.com and Human Writes also added “Claude watermark removal” features. Independent analysis of the released code showed that these tools currently address only Unicode artifacts and file metadata; none have demonstrated reliable removal of the statistical token bias.
The underlying technique is not new. It was introduced in the 2023 paper by Kirchenbauer et al. and later productised by Google as SynthID. OpenAI researcher Scott Aaronson described a similar approach in 2023, although it has not been deployed in ChatGPT. Because Anthropic has not published its detector or key-rotation policy, independent verification of removal claims remains impossible.
Research published at ICLR 2024 and NeurIPS 2024 confirms that even light paraphrasing or translation destroys most of the signal, while heavy rewriting reduces detection to random chance. Conversely, very short or rigidly structured outputs such as code snippets or tables contain too few token-choice opportunities for a reliable watermark to form.
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