超越闪烁：检测可泛化深度伪造视频检测中的运动不一致
Beyond Flicker: Detecting Kinematic Inconsistencies for Generalizable Deepfake Video Detection
AI总结：
本文提出合成视频生成方法，通过引入微妙的运动不一致来训练模型，从而提升深度伪造视频检测的泛化能力。
摘要：
将深度伪造检测推广到未见的篡改仍是一个关键挑战。最近的方法是通过训练网络使用经过手工制作的伪影篡改的纯净面部图像来提取更通用的线索。尽管在静态图像上有效，但将其扩展到视频领域仍是一个开放问题。现有方法将时间伪影建模为帧间不稳定性，忽视了一个关键漏洞：不同面部区域之间的自然运动依赖性被违反。在本文中，我们提出了一种合成视频生成方法，创建带有微妙运动不一致性的训练数据。我们训练了一个自动编码器，将面部地标配置分解为运动基底。通过操纵这些基底，我们有选择地打破面部运动中的自然相关性，并通过面部变形将这些伪影引入纯净视频中。一个在我们数据上训练的网络学会检测这些复杂的生物力学缺陷，在几个流行的基准上实现了最先进的泛化结果。
英文摘要：
Generalizing deepfake detection to unseen manipulations remains a key challenge. A recent approach to tackle this issue is to train a network with pristine face images that have been manipulated with hand-crafted artifacts to extract more generalizable clues. While effective for static images, extending this to the video domain is an open issue. Existing methods model temporal artifacts as frame-to-frame instabilities, overlooking a key vulnerability: the violation of natural motion dependencies between different facial regions. In this paper, we propose a synthetic video generation method that creates training data with subtle kinematic inconsistencies. We train an autoencoder to decompose facial landmark configurations into motion bases. By manipulating these bases, we selectively break the natural correlations in facial movements and introduce these artifacts into pristine videos via face morphing. A network trained on our data learns to spot these sophisticated biomechanical flaws, achieving state-of-the-art generalization results on several popular benchmarks.
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。
