通过创建时空规律破坏来检测深度伪造
Detecting Deepfake by Creating Spatio-Temporal Regularity Disruption
AI总结：
针对深度伪造检测泛化性差的问题，提出通过伪伪造生成器创建时空规律破坏的伪伪造视频进行训练，并设计时空增强块联合捕获时空破坏，在多个数据集上取得卓越性能。
摘要：
尽管深度伪造检测取得了令人鼓舞的进展，但由于训练期间探索的伪造线索有限，对未见伪造类型的泛化能力仍是一个重大挑战。相反，我们注意到深度伪造中的一个常见现象：伪造视频的创建不可避免地破坏了原始视频中的统计规律。受此观察启发，我们提出通过区分真实视频中不存在的“规律破坏”来提升深度伪造检测的泛化能力。具体而言，通过仔细检查空间和时间属性，我们提出通过伪伪造生成器破坏真实视频，并创建大量伪伪造视频用于训练。这种做法使我们能够在不使用伪造视频的情况下实现深度伪造检测，并以简单高效的方式提高泛化能力。为了联合捕获空间和时间破坏，我们提出了一个时空增强块，以在我们自创的视频上学习跨空间和时间的规律破坏。通过全面的实验，我们的方法在多个数据集上表现出卓越的性能。
英文摘要：
Despite encouraging progress in deepfake detection, generalization to unseen forgery types remains a significant challenge due to the limited forgery clues explored during training. In contrast, we notice a common phenomenon in deepfake: fake video creation inevitably disrupts the statistical regularity in original videos. Inspired by this observation, we propose to boost the generalization of deepfake detection by distinguishing the "regularity disruption" that does not appear in real videos. Specifically, by carefully examining the spatial and temporal properties, we propose to disrupt a real video through a Pseudo-fake Generator and create a wide range of pseudo-fake videos for training. Such practice allows us to achieve deepfake detection without using fake videos and improves the generalization ability in a simple and efficient manner. To jointly capture the spatial and temporal disruptions, we propose a Spatio-Temporal Enhancement block to learn the regularity disruption across space and time on our self-created videos. Through comprehensive experiments, our method exhibits excellent performance on several datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
