As generative AI advances, the distinction between authentic and synthetic media is increasingly blurred, challenging the integrity of online information. In this study, we present CONVEX, a large-scale dataset of multimodal misinformation involving miscaptioned, edited, and AI-generated visual content, comprising over 150K multimodal posts with associated notes and engagement metrics from X's Community Notes. We analyze how multimodal misinformation evolves in terms of virality, engagement, and consensus dynamics, with a focus on synthetic media. Our results show that while AI-generated content achieves disproportionate virality, its spread is driven primarily by passive engagement rather than active discourse. Despite slower initial reporting, AI-generated content reaches community consensus more quickly once flagged. Moreover, our evaluation of specialized detectors and vision-language models reveals a consistent decline in performance over time in distinguishing synthetic from authentic images as generative models evolve. These findings highlight the need for continuous monitoring and adaptive strategies in the rapidly evolving digital information environment.

翻译：随着生成式AI的进步，真实内容与合成媒体之间的界限日益模糊，对在线信息的真实性构成挑战。本研究提出CONVEX数据集——一个涵盖图片误配、编辑与AI生成视觉内容的大规模多模态虚假信息数据集，包含超过15万条多模态帖文及其关联注释与来自X社区笔记的互动数据。我们从病毒性传播、参与度与共识动力学角度分析多模态虚假信息的演变规律，重点关注合成媒体。结果表明，虽然AI生成内容获得不成比例的病毒式传播，但其扩散主要由被动参与而非主动讨论驱动。尽管初始举报速度较慢，但被标记后AI生成内容达成社区共识的速度更快。此外，对专用检测器与视觉语言模型的评估显示，随着生成模型进化，其在区分合成图像与真实图像方面的性能随时间持续下降。这些发现揭示了在快速演变的数字信息环境中持续监测与适应性策略的必要性。