发现大模型生成的低质新闻可骗过现有检测，提出新防御框架。
Exposing Pink Slime Journalism: Linguistic Signatures and Robust Detection Against LLM-Generated Threats
- 分析低质新闻的语言风格特征，识别其生成模式。
- 消费级大模型使检测准确率下降最高达40%。
- 设计抗大模型攻击的新框架，提升检测效果27%。
地方新闻对2800万美国人而言是可靠信息的重要来源，正面临名为‘粉红泥新闻’（Pink Slime Journalism）的威胁——即模仿真实地方报道的低质量自动文章。本文通过细致分析其语言、风格和词汇特征，揭示此类内容的独特模式，并提出针对性检测策略。研究发现，除传统生成方式外，利用大型语言模型（LLMs）进行修改已成为新型对抗手段：即使普通用户可访问的LLM也能显著削弱现有检测系统性能，导致F1分数最高下降40%。为此，我们提出一种专为抵御LLM对抗攻击而设计的鲁棒学习框架，在不断演化的自动化粉红泥新闻环境中表现出更强适应性，检测性能最高提升27%。
原文摘要 · Abstract (English)
The local news landscape, a vital source of reliable information for 28 million Americans, faces a growing threat from Pink Slime Journalism, a low-quality, auto-generated articles that mimic legitimate local reporting. Detecting these deceptive articles requires a fine-grained analysis of their linguistic, stylistic, and lexical characteristics. In this work, we conduct a comprehensive study to uncover the distinguishing patterns of Pink Slime content and propose detection strategies based on these insights. Beyond traditional generation methods, we highlight a new adversarial vector: modifications through large language models (LLMs). Our findings reveal that even consumer-accessible LLMs can significantly undermine existing detection systems, reducing their performance by up to 40% in F1-score. To counter this threat, we introduce a robust learning framework specifically designed to resist LLM-based adversarial attacks and adapt to the evolving landscape of automated pink slime journalism, and showed and improvement by up to 27%.
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