This research introduces a novel dual-detection watermarking technique that enhances security for large language model outputs by simultaneously detecting modifications and verifying text origin.

• Addresses a critical gap in current watermarking approaches that only focus on ownership verification
• Creates a watermarking scheme resistant to both modification and spoofing attacks
• Implements an innovative two-stage verification process that maintains high detection accuracy
• Demonstrates practical viability with minimal impact on text quality

This advancement provides crucial security infrastructure as LLMs become more prevalent, helping prevent malicious actors from manipulating watermarked text to produce harmful content while maintaining attribution.

Modification and Generated-Text Detection: Achieving Dual Detection Capabilities for the Outputs of LLM by Watermark