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.

翻译：生成式人工智能（GenAI）的发展已将虚假信息的生产从人工捏造转变为自动化、大规模操纵。本文呈现了首轮纵向专家认知调查（N=21）的结果，该调查涵盖了人工智能研究人员、政策制定者和虚假信息专家。研究考察了多模态威胁——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，虽然深度伪造视频具有直接的“冲击”价值，但大规模文本生成则构成了“认知碎片化”和“合成共识”的系统性风险，尤其在政治领域。调查揭示了专家对技术检测工具的怀疑态度，尽管存在实施障碍，他们更倾向于溯源标准和监管框架。生成式人工智能虚假信息研究需要可复现的方法。当前的挑战在于度量：缺乏标准化的基准和可复现性检查清单，追踪或对抗合成媒体仍然困难。我们建议将信息完整性视为一种基础设施，并在数据溯源和方法可复现性上保持严谨。