Can AI-Generated Text be Reliably Detected?

• URL: http://arxiv.org/abs/2303.11156v4
• Date: Fri, 17 Jan 2025 04:21:47 GMT
• Title: Can AI-Generated Text be Reliably Detected?
• Authors: Vinu Sankar Sadasivan, Aounon Kumar, Sriram Balasubramanian, Wenxiao Wang, Soheil Feizi,
• Abstract summary: Large Language Models (LLMs) perform impressively well in various applications.<n>The potential for misuse of these models in activities such as plagiarism, generating fake news, and spamming has raised concern about their responsible use.<n>We stress-test the robustness of these AI text detectors in the presence of an attacker.
• Score: 50.95804851595018
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) perform impressively well in various applications. However, the potential for misuse of these models in activities such as plagiarism, generating fake news, and spamming has raised concern about their responsible use. Consequently, the reliable detection of AI-generated text has become a critical area of research. AI text detectors have shown to be effective under their specific settings. In this paper, we stress-test the robustness of these AI text detectors in the presence of an attacker. We introduce recursive paraphrasing attack to stress test a wide range of detection schemes, including the ones using the watermarking as well as neural network-based detectors, zero shot classifiers, and retrieval-based detectors. Our experiments conducted on passages, each approximately 300 tokens long, reveal the varying sensitivities of these detectors to our attacks. Our findings indicate that while our recursive paraphrasing method can significantly reduce detection rates, it only slightly degrades text quality in many cases, highlighting potential vulnerabilities in current detection systems in the presence of an attacker. Additionally, we investigate the susceptibility of watermarked LLMs to spoofing attacks aimed at misclassifying human-written text as AI-generated. We demonstrate that an attacker can infer hidden AI text signatures without white-box access to the detection method, potentially leading to reputational risks for LLM developers. Finally, we provide a theoretical framework connecting the AUROC of the best possible detector to the Total Variation distance between human and AI text distributions. This analysis offers insights into the fundamental challenges of reliable detection as language models continue to advance. Our code is publicly available at https://github.com/vinusankars/Reliability-of-AI-text-detectors.

Related papers

• AuthorMist: Evading AI Text Detectors with Reinforcement Learning [4.806579822134391]
  AuthorMist is a novel reinforcement learning-based system to transform AI-generated text into human-like writing. We show that AuthorMist effectively reduces the detectability of AI-generated text while preserving the original meaning.
  arXiv  Detail & Related papers  (2025-03-10T12:41:05Z)
• Adversarial Attacks on AI-Generated Text Detection Models: A Token Probability-Based Approach Using Embeddings [14.150011713654331]
  This work proposes a novel textual adversarial attack on the detection models such as Fast-DetectGPT. The method employs embedding models for data perturbation, aiming at reconstructing the AI generated texts to reduce the likelihood of detection of the true origin of the texts.
  arXiv  Detail & Related papers  (2025-01-31T10:06:27Z)
• DAMAGE: Detecting Adversarially Modified AI Generated Text [0.13108652488669736]
  We show that many existing AI detectors fail to detect humanized text. We demonstrate a robust model that can detect humanized AI text while maintaining a low false positive rate.
  arXiv  Detail & Related papers  (2025-01-06T23:43:49Z)
• DetectRL: Benchmarking LLM-Generated Text Detection in Real-World Scenarios [38.952481877244644]
  We present a new benchmark, DetectRL, highlighting that even state-of-the-art (SOTA) detection techniques still underperformed in this task. Our development of DetectRL reveals the strengths and limitations of current SOTA detectors. We believe DetectRL could serve as an effective benchmark for assessing detectors in real-world scenarios.
  arXiv  Detail & Related papers  (2024-10-31T09:01:25Z)
• ESPERANTO: Evaluating Synthesized Phrases to Enhance Robustness in AI Detection for Text Origination [1.8418334324753884]
  This paper introduces back-translation as a novel technique for evading detection. We present a model that combines these back-translated texts to produce a manipulated version of the original AI-generated text. We evaluate this technique on nine AI detectors, including six open-source and three proprietary systems.
  arXiv  Detail & Related papers  (2024-09-22T01:13:22Z)
• MOSAIC: Multiple Observers Spotting AI Content, a Robust Approach to Machine-Generated Text Detection [35.67613230687864]
  Large Language Models (LLMs) are trained at scale and endowed with powerful text-generating abilities. Various proposals have been made to automatically discriminate artificially generated from human-written texts. We derive a new, theoretically grounded approach to combine their respective strengths. Our experiments, using a variety of generator LLMs, suggest that our method effectively leads to robust detection performances.
  arXiv  Detail & Related papers  (2024-09-11T20:55:12Z)
• SilverSpeak: Evading AI-Generated Text Detectors using Homoglyphs [0.0]
  Homoglyph-based attacks can effectively circumvent state-of-the-art AI-generated text detectors. Our findings demonstrate that homoglyph-based attacks can effectively circumvent state-of-the-art detectors.
  arXiv  Detail & Related papers  (2024-06-17T06:07:32Z)
• The Impact of Prompts on Zero-Shot Detection of AI-Generated Text [4.337364406035291]
  In chat-based applications, users commonly input prompts and utilize the AI-generated texts. We introduce an evaluative framework to empirically analyze the impact of prompts on the detection accuracy of AI-generated text.
  arXiv  Detail & Related papers  (2024-03-29T11:33:34Z)
• Assaying on the Robustness of Zero-Shot Machine-Generated Text Detectors [57.7003399760813]
  We explore advanced Large Language Models (LLMs) and their specialized variants, contributing to this field in several ways. We uncover a significant correlation between topics and detection performance. These investigations shed light on the adaptability and robustness of these detection methods across diverse topics.
  arXiv  Detail & Related papers  (2023-12-20T10:53:53Z)
• Towards Possibilities & Impossibilities of AI-generated Text Detection: A Survey [97.33926242130732]
  Large Language Models (LLMs) have revolutionized the domain of natural language processing (NLP) with remarkable capabilities of generating human-like text responses. Despite these advancements, several works in the existing literature have raised serious concerns about the potential misuse of LLMs. To address these concerns, a consensus among the research community is to develop algorithmic solutions to detect AI-generated text.
  arXiv  Detail & Related papers  (2023-10-23T18:11:32Z)
• SeqXGPT: Sentence-Level AI-Generated Text Detection [62.3792779440284]
  We introduce a sentence-level detection challenge by synthesizing documents polished with large language models (LLMs) We then propose textbfSequence textbfX (Check) textbfGPT, a novel method that utilizes log probability lists from white-box LLMs as features for sentence-level AIGT detection.
  arXiv  Detail & Related papers  (2023-10-13T07:18:53Z)
• Watermarking Conditional Text Generation for AI Detection: Unveiling Challenges and a Semantic-Aware Watermark Remedy [52.765898203824975]
  We introduce a semantic-aware watermarking algorithm that considers the characteristics of conditional text generation and the input context. Experimental results demonstrate that our proposed method yields substantial improvements across various text generation models.
  arXiv  Detail & Related papers  (2023-07-25T20:24:22Z)
• OUTFOX: LLM-Generated Essay Detection Through In-Context Learning with Adversarially Generated Examples [44.118047780553006]
  OUTFOX is a framework that improves the robustness of LLM-generated-text detectors by allowing both the detector and the attacker to consider each other's output. Experiments show that the proposed detector improves the detection performance on the attacker-generated texts by up to +41.3 points F1-score. The detector shows a state-of-the-art detection performance: up to 96.9 points F1-score, beating existing detectors on non-attacked texts.
  arXiv  Detail & Related papers  (2023-07-21T17:40:47Z)
• MAGE: Machine-generated Text Detection in the Wild [82.70561073277801]
  Large language models (LLMs) have achieved human-level text generation, emphasizing the need for effective AI-generated text detection. We build a comprehensive testbed by gathering texts from diverse human writings and texts generated by different LLMs. Despite challenges, the top-performing detector can identify 86.54% out-of-domain texts generated by a new LLM, indicating the feasibility for application scenarios.
  arXiv  Detail & Related papers  (2023-05-22T17:13:29Z)
• On the Possibilities of AI-Generated Text Detection [76.55825911221434]
  We argue that as machine-generated text approximates human-like quality, the sample size needed for detection bounds increases. We test various state-of-the-art text generators, including GPT-2, GPT-3.5-Turbo, Llama, Llama-2-13B-Chat-HF, and Llama-2-70B-Chat-HF, against detectors, including oBERTa-Large/Base-Detector, GPTZero.
  arXiv  Detail & Related papers  (2023-04-10T17:47:39Z)
• Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense [56.077252790310176]
  We present a 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. We introduce a simple defense that relies on retrieving semantically-similar generations and must be maintained by a language model API provider.
  arXiv  Detail & Related papers  (2023-03-23T16:29:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.