The rapid proliferation of AI-generated content, driven by advances in generative adversarial networks, diffusion models, and multimodal large language models, has made the creation and dissemination of synthetic media effortless, heightening the risks of misinformation, particularly political deepfakes that distort truth and undermine trust in political institutions. In turn, governments, research institutions, and industry have strongly promoted deepfake detection initiatives as solutions. Yet, most existing models are trained and validated on synthetic, laboratory-controlled datasets, limiting their generalizability to the kinds of real-world political deepfakes circulating on social platforms that affect the public. In this work, we introduce the first systematic benchmark based on the Political Deepfakes Incident Database, a curated collection of real-world political deepfakes shared on social media since 2018. Our study includes a systematic evaluation of state-of-the-art deepfake detectors across academia, government, and industry. We find that the detectors from academia and government perform relatively poorly. While paid detection tools achieve relatively higher performance than free-access models, all evaluated detectors struggle to generalize effectively to authentic political deepfakes, and are vulnerable to simple manipulations, especially in the video domain. Results urge the need for politically contextualized deepfake detection frameworks to better safeguard the public in real-world settings.

翻译：随着生成对抗网络、扩散模型和多模态大语言模型的进步，AI生成内容迅速扩散，使得合成媒体的创建与传播变得毫不费力，加剧了错误信息的风险，尤其是扭曲事实、削弱政治机构信任的政治性深度伪造内容。对此，政府、研究机构和产业界大力推动深度伪造检测计划作为应对方案。然而，现有大多数模型均在实验室控制的合成数据集上训练和验证，限制了其对社交媒体上传播、影响公众的现实世界政治性深度伪造内容的泛化能力。本研究基于政治深度伪造事件数据库——一个自2018年以来在社交媒体上分享的真实世界政治性深度伪造内容精选集合——首次引入系统性基准。我们的研究包括对学术界、政府和产业界最先进的深度伪造检测器进行系统评估。我们发现，学术界和政府开发的检测器表现相对较差。尽管付费检测工具比免费访问模型取得了相对更高的性能，但所有评估的检测器均难以有效泛化至真实的政治性深度伪造内容，且易受简单操作的影响，尤其是在视频领域。研究结果强调，需要建立具有政治语境适应性的深度伪造检测框架，以在现实世界环境中更好地保护公众。