mlsanityData to 14 Oct 2026

J-Guard: Journalism Guided Adversarially Robust Detection of AI-generated News

arXiv 2309.03164 ↗First posted 6 Sep 2023cs.CL cs.AIcited by 10

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Abstract (arXiv)

The rapid proliferation of AI-generated text online is profoundly reshaping the information landscape. Among various types of AI-generated text, AI-generated news presents a significant threat as it can be a prominent source of misinformation online. While several recent efforts have focused on detecting AI-generated text in general, these methods require enhanced reliability, given concerns about their vulnerability to simple adversarial attacks. Furthermore, due to the eccentricities of news writing, applying these detection methods for AI-generated news can produce false positives, potentially damaging the reputation of news organizations. To address these challenges, we leverage the expertise of an interdisciplinary team to develop a framework, J-Guard, capable of steering existing supervised AI text detectors for detecting AI-generated news while boosting adversarial robustness. By incorporating stylistic cues inspired by the unique journalistic attributes, J-Guard effectively distinguishes between real-world journalism and AI-generated news articles. Our experiments on news articles generated by a vast array of AI models, including ChatGPT (GPT3.5), demonstrate the effectiveness of J-Guard in enhancing detection capabilities while maintaining an average performance decrease of as low as 7% when faced with adversarial attacks.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 20 of its 34 references that are in the map. First posted on arXiv 6 Sep 2023.

Builds on

20 of 34 references.

References · 20

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8 more
  1. 2020Automatic Detection of Machine Generated Text: A Critical Surveycited by 166
  2. 2023Generative Language Models and Automated Influence Operations: Emerging Threats and Potential Mitigationscited by 158
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Cited by year

Citing papers per year: 2023: 2; 2024: 5; 2025: 3.

10 papers.

  1. 2023Can Knowledge Graphs Reduce Hallucinations in LLMs? : A Surveycited by 92
  2. 2024Detecting Multimedia Generated by Large AI Models: A Surveycited by 76
  3. 2024Detecting AI-Generated Text: Factors Influencing Detectability with Current Methodscited by 23
  4. 2024A Survey of AI-generated Text Forensic Systems: Detection, Attribution, and Characterizationcited by 19
  5. 2023TOPFORMER: Topology-Aware Authorship Attribution of Deepfake Texts with Diverse Writing Stylescited by 10
  6. 2025A Training-free Method for LLM Text Attributioncited by 5
  7. 2025Explainability-Based Token Replacement on LLM-Generated Textcited by 3
  8. 2024Model Attribution in LLM-Generated Disinformation: A Domain Generalization Approach with Supervised Contrastive Learningcited by 2
  9. 2025Zero-Shot Attribution for Large Language Models: A Distribution Testing Approachcited by 1
  10. 2024Counter Turing Test (CT2): Investigating AI-Generated Text Detection for Hindi -- Ranking LLMs based on Hindi AI Detectability Index (ADIhi)cited by 0