Watermark Collision

Watermark collision in large language models (LLMs) is a critical challenge in verifying the origin of AI-generated text, focusing on the unintended overlap or conflict of watermarks embedded by different methods or keys. Current research investigates the vulnerabilities of various watermarking techniques, including logit-based approaches and statistical methods, highlighting issues like key collisions leading to distribution bias and the potential for watermark stealing through API queries. These findings underscore the need for more robust and secure watermarking schemes to effectively address copyright concerns and ensure the reliable identification of AI-generated content.