arXiv:2606.31602v1 Announce Type: new Abstract: This work presents Dual-Embedding Watermarking (DEW), a semantic watermarking scheme for large language models (LLMs) that leverages contextual and token-level embeddings to enhance robustness against paraphrasing and translation. DEW utilizes a signal-processing methodology, applying algebraic vector-space operations to \mbox{token and context embeddings to derive a watermark signal that degrades gracefully under semantic shifts. The method obfuscates the watermark by projecting embedding vectors through pseudo-random matrices seeded with a secr
The paper addresses a critical and immediate need for robust mechanisms to identify AI-generated content amidst the proliferation of LLMs and increasing concerns about authenticity and mis/disinformation.
Sophisticated watermarking techniques are essential for maintaining trust in digital information, attribution, and regulating the responsible deployment of AI while combating risks like deepfakes and AI-driven propaganda.
This advancement shifts the landscape towards more resilient AI content verification, making it harder for malicious actors to remove watermarks through common manipulation tactics like paraphrasing or translation.
- · Platforms and Media Companies
- · Regulatory Bodies
- · Ethical AI Developers
- · Content Authenticity Initiatives
- · Disinformation Networks
- · Unscrupulous Content Creators
- · AI Models Lacking Attribution Tools
Improved detection of AI-generated text, enhancing trust and attribution for LLM outputs.
Increased pressure for AI developers to integrate robust watermarking into their models, potentially influencing regulatory standards.
A potential arms race between watermarking techniques and adversarial attacks designed to remove them, leading to continuous evolution in both fields.
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Read at arXiv cs.CL