Papers
Topics
Authors
Recent
Search
2000 character limit reached

Provable Robust Watermarking for AI-Generated Text

Published 30 Jun 2023 in cs.CL and cs.LG | (2306.17439v2)

Abstract: We study the problem of watermarking LLMs generated text -- one of the most promising approaches for addressing the safety challenges of LLM usage. In this paper, we propose a rigorous theoretical framework to quantify the effectiveness and robustness of LLM watermarks. We propose a robust and high-quality watermark method, Unigram-Watermark, by extending an existing approach with a simplified fixed grouping strategy. We prove that our watermark method enjoys guaranteed generation quality, correctness in watermark detection, and is robust against text editing and paraphrasing. Experiments on three varying LLMs and two datasets verify that our Unigram-Watermark achieves superior detection accuracy and comparable generation quality in perplexity, thus promoting the responsible use of LLMs. Code is available at https://github.com/XuandongZhao/Unigram-Watermark.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (48)
  1. A watermark for large language models. International Conference on Machine Learning, 2023.
  2. Language models are few-shot learners. Advances in neural information processing systems, 33:1877–1901, 2020.
  3. Hierarchical text-conditional image generation with clip latents. ArXiv, abs/2204.06125, 2022.
  4. Photorealistic text-to-image diffusion models with deep language understanding. Advances in Neural Information Processing Systems, 35:36479–36494, 2022.
  5. OpenAI. Gpt-4 technical report. ArXiv, abs/2303.08774, 2023a.
  6. OpenAI. Chatgpt: Optimizing language models for dialogue. OpenAI blog, 2022. URL https://openai.com/blog/chatgpt/.
  7. Defending against neural fake news. Advances in neural information processing systems, 32, 2019.
  8. Ethical and social risks of harm from language models. ArXiv, abs/2112.04359, 2021.
  9. Chris Stokel-Walker. Ai bot chatgpt writes smart essays - should professors worry? Nature, 2022.
  10. Robust speech recognition via large-scale weak supervision. ArXiv, abs/2212.04356, 2022.
  11. Poisoning web-scale training datasets is practical. ArXiv, abs/2302.10149, 2023.
  12. Alan M Turing. Computing machinery and intelligence. 1950.
  13. Gltr: Statistical detection and visualization of generated text. In Annual Meeting of the Association for Computational Linguistics, 2019.
  14. Detectgpt: Zero-shot machine-generated text detection using probability curvature. ArXiv, abs/2301.11305, 2023.
  15. Dirk Hovy. The enemy in your own camp: How well can we detect statistically-generated fake reviews – an adversarial study. In Annual Meeting of the Association for Computational Linguistics, 2016.
  16. OpenAI. New ai classifier for indicating ai-written text. OpenAI blog, 2023b. URL https://openai.com/blog/new-ai-classifier-for-indicating-ai-written-text.
  17. Gpt detectors are biased against non-native english writers. ArXiv, abs/2304.02819, 2023.
  18. Protecting language generation models via invisible watermarking. ArXiv, abs/2302.03162, 2023.
  19. Scott Aaronson. Simons institute talk on watermarking of large language models, 2023. URL https://simons.berkeley.edu/talks/scott-aaronson-ut-austin-openai-2023-08-17.
  20. Undetectable watermarks for language models. arXiv preprint arXiv:2306.09194, 2023.
  21. Mélisande Albert. Concentration inequalities for randomly permuted sums. In High Dimensional Probability VIII: The Oaxaca Volume, pages 341–383. Springer, 2019.
  22. Language models are unsupervised multitask learners. OpenAI blog, 2019.
  23. Attention is all you need. Advances in neural information processing systems, 30, 2017.
  24. Roberta: A robustly optimized bert pretraining approach. ArXiv, abs/1907.11692, 2019.
  25. Calibrating noise to sensitivity in private data analysis. In Theory of cryptography, pages 265–284. Springer, 2006.
  26. Optimal differential privacy composition for exponential mechanisms. In International Conference on Machine Learning, pages 2597–2606. PMLR, 2020.
  27. Paraphrasing evades detectors of ai-generated text, but retrieval is an effective defense. ArXiv, abs/2303.13408, 2023.
  28. Pointer sentinel mixture models. In International Conference on Learning Representations, 2017.
  29. Opt: Open pre-trained transformer language models. ArXiv, abs/2205.01068, 2022.
  30. Llama: Open and efficient foundation language models. ArXiv, abs/2302.13971, 2023.
  31. The curious case of neural text degeneration. In International Conference on Learning Representations, 2020.
  32. Huggingface’s transformers: State-of-the-art natural language processing. ArXiv, abs/1910.03771, 2019.
  33. Training language models to follow instructions with human feedback. ArXiv, abs/2203.02155, 2022.
  34. Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. In Annual Meeting of the Association for Computational Linguistics, 2019.
  35. Teaching machines to read and comprehend. Advances in neural information processing systems, 28, 2015.
  36. Information hiding techniques for steganography and digital watermarking, 2000.
  37. The hiding virtues of ambiguity: quantifiably resilient watermarking of natural language text through synonym substitutions. In Workshop on Multimedia & Security, 2006.
  38. Natural language watermarking: Design, analysis, and a proof-of-concept implementation. In Information Hiding, 2001.
  39. Natural language watermarking and tamperproofing. In Information Hiding, 2002.
  40. Tracing text provenance via context-aware lexical substitution. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pages 11613–11621, 2022.
  41. Frustratingly easy edit-based linguistic steganography with a masked language model. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2021.
  42. On pushing deepfake tweet detection capabilities to the limits. Proceedings of the 14th ACM Web Science Conference 2022, 2022.
  43. Max Wolff. Attacking neural text detectors. ArXiv, abs/2002.11768, 2020.
  44. Can ai-generated text be reliably detected? ArXiv, abs/2303.11156, 2023.
  45. On the possibilities of ai-generated text detection. arXiv preprint arXiv:2304.04736, 2023.
  46. Exploring the limits of transfer learning with a unified text-to-text transformer. The Journal of Machine Learning Research, 21(1):5485–5551, 2020.
  47. Mechanism design via differential privacy. In Symposium on Foundations of Computer Science (FOCS’07), pages 94–103. IEEE, 2007.
  48. Bounding, concentrating, and truncating: Unifying privacy loss composition for data analytics. In Algorithmic Learning Theory, pages 421–457. PMLR, 2021.
Citations (122)
View on Semantic Scholar

Summary

  • The paper presents a unique provable framework that rigorously defines metrics for watermark effectiveness in AI-generated text.
  • The Unigram-Watermark method groups vocabulary into 'green' and 'red' lists, significantly enhancing resilience against text edits and paraphrasing.
  • Experimental results show superior detection accuracy and maintained text quality, validating the method's practical utility in secure AI text generation.

Provable Robust Watermarking for AI-Generated Text

The paper "Provable Robust Watermarking for AI-Generated Text" presents a sophisticated framework for watermarking text generated by LLMs. This research is driven by the necessity to identify and verify AI-generated text, addressing safety concerns and potential misuse. The authors introduce the Unigram-Watermark method, which builds on existing watermarking strategies by enhancing robustness against text editing and paraphrasing while maintaining high-quality text generation.

Core Contributions

  1. Theoretical Framework: The paper offers a robust theoretical framework to evaluate the effectiveness of watermarks in AI-generated text. It emphasizes a precise definition of performance, correctness, and resilience against post-processing manipulations, thereby addressing potential vulnerabilities.
  2. Unigram-Watermark Method: The authors present a new watermarking method, the Unigram-Watermark, which extends and refines prior techniques. This method uses a uniform fixed grouping of the vocabulary into a 'green list' and a 'red list,' enhancing resilience against common alterations like synonyms replacement or text paraphrasing. The method ensures that watermarked text remains statistically close to un-watermarked text with bounded Renyi-divergence for all orders.
  3. Experiments and Results: Comprehensive experiments using three LLMs and two datasets illustrate the superior detection accuracy and robustness of the Unigram-Watermark. The experiments confirm the method's high detection accuracy and improved text generation quality, quantified via perplexity scores, without significant degradation.

Key Findings

  • Numerical Results: Empirical findings reveal that the Unigram-Watermark achieves a detection accuracy that surpasses previous watermarking techniques while maintaining comparable text generation quality. Specifically, the perplexity scores of watermarked texts remain close to that of un-watermarked texts, mitigating concerns about quality degradation.
  • Robustness to Edits: The Unigram-Watermark's proof of robustness against arbitrary edits highlights its resilience. With provable guarantees, it can withstand a specified number of text edits without compromising watermark detection.
  • Generalizability: The robustness and efficiency of Unigram-Watermark suggest that its advantages might extend to improving security practices for detecting AI-generated texts beyond its primary design contexts, particularly in areas involving high-stakes manipulations, such as legal document generation and educational assessments.

Implications and Future Directions

The introduction of robust watermark techniques like Unigram-Watermark signifies progress in the field of AI ethics and safety, creating pathways for more secure interactions with AI text generation systems. This technology could become instrumental in mitigating risks associated with fraudulent AI uses, safeguarding intellectual property, and fostering trust in public AI outputs.

Future Research: Further research could investigate cryptographically secure watermarking methods to complement the statistically robust frameworks presented. The challenge of balancing robustness against attack and maintaining low watermark learnability presents a compelling research avenue. Moreover, exploring adaptive watermark strategies that dynamically respond to the evolving techniques used to attack watermark systems could offer enhanced security outcomes.

This work offers a comprehensive solution for embedding and detecting watermarks in AI-generated text, thereby supporting responsible AI usage. It advances theoretical understanding while offering practical tools to reinforce security in LLM outputs, setting a foundation for responsible AI evolution.

Paper to Video (Beta)

Whiteboard

No one has generated a whiteboard explanation for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Sign Up to Generate

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Generate Now

Collections

Sign up for free to add this paper to one or more collections.

User Circle Single Streamline Icon: https://streamlinehq.com Sign Up

Tweets

Sign up for free to view the 1 tweet with 3 likes about this paper.

User Circle Single Streamline Icon: https://streamlinehq.com Sign Up for Free