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The author discovered that inserting invisible Unicode variation selectors can effectively remove text watermarks in Claude and other AI models, based on extensive testing and benchmarking across multiple open models.
This article presents an educational resource detailing how AI text watermarking functions and methods to evade it, triggered by recent announcements from Anthropic and the European Commission on marking AI-generated content.
Anthropic explains how its new watermarking technology for Claude AI works, designed to comply with EU AI Act transparency requirements using the SynthID-Text approach from Google DeepMind.
The article argues that text AI watermarks are inherently trivial to remove, exploring the EU AI Act's watermarking requirements and the technical challenges of text steganography, including Google's SynthID approach.
This paper presents Dual-Embedding Watermarking (DEW), a semantic watermarking scheme for LLMs that improves robustness against paraphrasing and translation by leveraging contextual and token-level embeddings. Experimental results show improved detection after paraphrasing and translation compared to prior methods.