论文标题
实用的深层检测：全球环境中的脆弱性
Practical Deepfake Detection: Vulnerabilities in Global Contexts
论文作者
论文摘要
深度学习的最新进展使视频（被称为深击）实现了现实的数字变化。这项技术引起了关于虚假和真实性的重要社会关注，使许多深泡检测算法的发展充满了动力。同时，培训数据和野外视频数据之间存在显着差异，这可能会破坏其实际功效。我们模拟了数据损坏技术，并检查了FaceForensics ++数据集的损坏变体上最先进的深层检测算法的性能。 尽管DeepFake检测模型与与培训时间增加一致的视频腐败具有强大的态度，但我们发现它们仍然容易受到视频腐败的影响，这些腐败模拟视频质量的下降。的确，在加蓬总统邦戈（Bongo）的新年地址的视频中，自信地验证了原始视频的算法，该算法评判了视频的高度损坏的变体是假的。我们的工作在全球背景下对实践深泡探测进行了探索的技术和道德途径。
Recent advances in deep learning have enabled realistic digital alterations to videos, known as deepfakes. This technology raises important societal concerns regarding disinformation and authenticity, galvanizing the development of numerous deepfake detection algorithms. At the same time, there are significant differences between training data and in-the-wild video data, which may undermine their practical efficacy. We simulate data corruption techniques and examine the performance of a state-of-the-art deepfake detection algorithm on corrupted variants of the FaceForensics++ dataset. While deepfake detection models are robust against video corruptions that align with training-time augmentations, we find that they remain vulnerable to video corruptions that simulate decreases in video quality. Indeed, in the controversial case of the video of Gabonese President Bongo's new year address, the algorithm, which confidently authenticates the original video, judges highly corrupted variants of the video to be fake. Our work opens up both technical and ethical avenues of exploration into practical deepfake detection in global contexts.
