生成式人工智能的验证危机：专家对生成式人工智能虚假信息的看法及可重复性溯源的案例
The Verification Crisis: Expert Perceptions of GenAI Disinformation and the Case for Reproducible Provenance
AI总结：
本文探讨生成式人工智能虚假信息的验证危机，指出大规模文本生成带来的系统性风险，并提出通过可重复性溯源和监管框架来应对挑战。
摘要：
生成式人工智能（GenAI）的发展使虚假信息的生产从手动伪造转向了自动化的大规模操纵。本文呈现了第一波纵向专家感知调查（N=21）的发现，涉及人工智能研究人员、政策制定者和虚假信息专家。它考察了多模态威胁（文本、图像、音频和视频）的感知严重性，并评估了当前的缓解策略。结果表明，尽管深度伪造视频具有即时的“震惊”价值，但大规模文本生成却带来了系统性的“知识碎片化”和“合成共识”风险，尤其是在政治领域。调查揭示了对技术检测工具的怀疑，专家更倾向于采用溯源标准和监管框架，尽管存在实施障碍。生成式人工智能虚假信息研究需要可重复的方法。当前的挑战是测量：在没有标准化基准和可重复性检查清单的情况下，跟踪或对抗合成媒体仍然困难。我们提出将信息完整性视为基础设施，以严谨的数据溯源和方法论可重复性来应对。
英文摘要：
The growth of Generative Artificial Intelligence (GenAI) has shifted disinformation production from manual fabrication to automated, large-scale manipulation. This article presents findings from the first wave of a longitudinal expert perception survey (N=21) involving AI researchers, policymakers, and disinformation specialists. It examines the perceived severity of multimodal threats -- text, image, audio, and video -- and evaluates current mitigation strategies. Results indicate that while deepfake video presents immediate "shock" value, large-scale text generation poses a systemic risk of "epistemic fragmentation" and "synthetic consensus," particularly in the political domain. The survey reveals skepticism about technical detection tools, with experts favoring provenance standards and regulatory frameworks despite implementation barriers. GenAI disinformation research requires reproducible methods. The current challenge is measurement: without standardized benchmarks and reproducibility checklists, tracking or countering synthetic media remains difficult. We propose treating information integrity as an infrastructure with rigor in data provenance and methodological reproducibility.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
