分析15万条多媒体假信息，发现AI生成内容传播快但靠被动转发，检测难度持续上升。
The Synthetic Media Shift: Tracking the Rise, Virality, and Detectability of AI-Generated Multimodal Misinformation
- 构建包含15万条图文的虚假信息数据集，追踪内容演变与互动模式。
- AI生成内容虽初始报告慢，但被标记后共识形成更快，且传播更依赖被动点击。
- 主流检测模型性能随生成技术迭代持续下降，需动态更新防御策略。
随着生成式AI的发展，真实与合成媒体之间的界限日益模糊，威胁在线信息完整性。本研究提出CONVEX，一个涵盖超过15万条多媒体帖子的大规模虚假信息数据集，包含误标、编辑及AI生成的视觉内容，附有来自X平台社区笔记的注释与互动指标。我们分析了多模态虚假信息在传播力、互动行为和共识形成方面的演变，重点关注合成媒体。结果表明，尽管AI生成内容初始报告较慢，但其传播主要由被动参与驱动，一旦被标记，社区达成共识的速度更快。此外，对专用检测器和视觉-语言模型的评估显示，随着生成模型的演进，其区分合成与真实图像的能力持续下降。这些发现凸显了在快速变化的数字信息环境中持续监控与自适应策略的必要性。
原文摘要 · Abstract (English)
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.
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