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Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense
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The rise in malicious usage of large language models, such as fake content creation and academic plagiarism, has motivated the development of approaches that identify AI-generated text, including those based on watermarking or outlier detection. However, the robustness of these detection algorithms to paraphrases of AI-generated text remains unclear. To stress test these detectors, we build a 11B parameter paraphrase generation model (DIPPER) that can paraphrase paragraphs, condition on surrounding context, and control lexical diversity and content reordering. Using DIPPER to paraphrase text generated by three large language models (including GPT3.5-davinci-003) successfully evades several detectors, including watermarking, GPTZero, DetectGPT, and OpenAI's text classifier. For example, DIPPER drops detection accuracy of DetectGPT from 70.3% to 4.6% (at a constant false positive rate of 1%), without appreciably modifying the input semantics. To increase the robustness of AI-generated text detection to paraphrase attacks, we introduce a simple defense that relies on retrieving semantically-similar generations and must be maintained by a language model API provider. Given a candidate text, our algorithm searches a database of sequences previously generated by the API, looking for sequences that match the candidate text within a certain threshold. We empirically verify our defense using a database of 15M generations from a fine-tuned T5-XXL model and find that it can detect 80% to 97% of paraphrased generations across different settings while only classifying 1% of human-written sequences as AI-generated. We open-source our models, code and data.
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“On average, simple paraphrasing leads to a 30.27% relative drop in T@1%F.”
From Adversarial Paraphrasing: A Universal Attack for Humanizing AI-Generated Text (2025) · cites this paper as [15]
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“Rather than promoting ethical academic behavior, AI detection tools often encourage strategic avoidance tactics, such as paraphrasing services or altering sentence structures to bypass detection [15,23].”
From Evaluating the Effectiveness and Ethical Implications of AI Detection Tools in Higher Education (2025) · cites this paper as [23]
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“This undermines the goal of meaningful learning, as students are more focused on “passing” algorithmic checks than developing deeper critical thinking and reflective writing skills [23].”
From Evaluating the Effectiveness and Ethical Implications of AI Detection Tools in Higher Education (2025) · cites this paper as [23]
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“Sadasivan et al. [28] and Krishna et al. [15] demonstrated how AI-generated text can be paraphrased by another AI model to evade detection by the existing AI detectors.”
From Adversarial Paraphrasing: A Universal Attack for Humanizing AI-Generated Text (2025) · cites this paper as [15]
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“Krishna et al. [15] proposed DIPPER, a powerful T5-based [24] paraphrasing model that significantly enhances the effectiveness of such attacks.”
From Adversarial Paraphrasing: A Universal Attack for Humanizing AI-Generated Text (2025) · cites this paper as [15]
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“The limitations of traditional AI detection tools have prompted researchers to develop more sophisticated techniques, including many rooted in machine learning and natural language processing (NLP) [23].”
From Evaluating the Effectiveness and Ethical Implications of AI Detection Tools in Higher Education (2025) · cites this paper as [23]
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“Using a powerful paraphrasing model (DIPPER), they showed that the detection accuracy of advanced systems like DetectGPT plummeted from 70.3% to just 4.6% when outputs were paraphrased and measured at a fixed 1% false positive rate [23].”
From Evaluating the Effectiveness and Ethical Implications of AI Detection Tools in Higher Education (2025) · cites this paper as [23]
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“This underscores how surface-level linguistic features are easily manipulated by large language models (LLMs).”
From Evaluating the Effectiveness and Ethical Implications of AI Detection Tools in Higher Education (2025) · cites this paper as [23]
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“Although retrieval-based approaches have been shown to defend effectively against paraphrasing attacks (Krishna et al. 2023)”
From A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directions (2025) · cites this paper as Krishna et al. 2023
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“Empirical evidence attests to the superior performance of the OUTFOX method relative to preceding statistical methods and those based on RoBERTa, particularly in responding to attacks from TF-IDF and DIPPER (Krishna et al. 2023).”
From A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directions (2025) · cites this paper as Krishna et al. 2023
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Metadata
- Authors
- Krishna, K., Song, Y., Karpinska, M., Wieting, J., & Iyyer, M.
- Venue
- Advances in Neural Information Processing Systems 36
- Published
- 2023
- Pages
- 27469-27500
- ISSN
- 1049-5258 (ISSN-L)
- OpenAlex
- W7133209433
- Cited by
- 150
- References
- 0
- Citations by year
- 2026: 55 · 2025: 33 · 2024: 36 · 2023: 25
- FWCI
- 26.40
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- neural information processing systems (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Authorship Attribution and Profiling
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Keywords
- paraphrase, AI-generated text detection, text retrieval, large language models
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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