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Detecting AI-generated scientific reviews is challenging. This study introduces a novel watermarking technique embedded in PDFs to reliably identify large language model (LLM)-assisted reviews, even against defenses.

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Area of Science:

  • Artificial Intelligence
  • Scientific Publishing
  • Information Security

Background:

  • The integrity of scientific peer review is crucial for progress.
  • Large language models (LLMs) pose a risk, with potential for AI-generated reviews.
  • Current detection tools struggle to differentiate AI-assisted from human reviews.

Purpose of the Study:

  • To develop a robust method for detecting LLM-generated peer reviews.
  • To address the limitations of existing detection tools in identifying AI-assisted content.
  • To ensure the reliability and authenticity of the scientific peer review process.

Main Methods:

  • Implementing indirect prompt injection via PDF to embed covert watermarks in LLM-generated reviews.
  • Developing novel watermarking schemes and hypothesis tests with strong statistical guarantees.
  • Controlling the family-wise error rate across multiple reviews for enhanced statistical power.
  • Evaluating multiple indirect prompt injection strategies, including font-based embedding and obfuscated prompts.
  • Testing resilience against common reviewer defenses and validating statistical bounds in practice.

Main Results:

  • High success rates achieved in embedding watermarks across various LLMs.
  • The proposed watermarking approach demonstrated resilience against common reviewer defenses.
  • Statistical tests maintained accuracy in practice, outperforming conservative methods like Bonferroni correction.
  • The framework offers strong statistical guarantees for detecting AI-generated reviews.

Conclusions:

  • A rigorous watermarking and detection framework effectively identifies LLM-generated peer reviews.
  • The method provides reliable detection with strong statistical assurances, crucial for scientific integrity.
  • This approach advances the ability to distinguish AI-assisted content, safeguarding the peer review process.