系统梳理生成式AI时代的伪造媒体技术，揭示当前检测方法的泛化短板。
Deepfake Media Generation and Detection in the Generative AI Era: A Survey and Outlook
- 构建多模态伪造内容分类体系，涵盖图像、视频、音频等类型。
- 新基准测试显示：顶尖检测器对未见过的生成器失效。
- 适合关注伪造内容安全与模型鲁棒性的研究人员参考。
本文系统调研了生成式AI时代下的深度伪造生成与检测技术，覆盖图像、视频、音频及多模态内容。通过识别各类深度伪造形式，构建了生成与检测方法的分类体系，阐明关键方法类别。收集主流检测数据集，更新了各检测器在常用数据集上的性能排名。此外，提出一个新颖的多模态基准，用于评估检测器在分布外（out-of-distribution）内容上的表现。结果表明，现有最先进检测器无法有效泛化至未知生成器产生的伪造内容。项目主页与新基准已开源：https://github.com/CroitoruAlin/biodeep。
原文摘要 · Abstract (English)
We survey deepfake generation and detection techniques, covering all deepfake media types: image, video, audio and multimodal content. We identify various kinds of deepfakes and construct taxonomies of deepfake generation and detection methods, illustrating the important groups of methods. Next, we gather datasets used for deepfake detection and provide updated rankings of the best performing detectors on the most popular datasets. In addition, we develop a novel multimodal benchmark to evaluate deepfake detectors on out-of-distribution content. The results indicate that state-of-the-art detectors fail to generalize to deepfakes generated by unseen generators. Our project page and new benchmark are available at https://github.com/CroitoruAlin/biodeep.
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