论文标题
对抗性深：评估深料检测器对对抗性例子的脆弱性
Adversarial Deepfakes: Evaluating Vulnerability of Deepfake Detectors to Adversarial Examples
论文作者
论文摘要
视频操纵技术的最新进展使一代假视频的一代比以往任何时候都更容易访问。操纵视频可以推动虚假信息并减少对媒体的信任。因此，伪造视频的检测引起了学术界和工业的极大兴趣。最近开发的DeepFake检测方法依靠深神经网络（DNN）来区分AI生成的假视频与真实视频。在这项工作中，我们证明可以通过使用现有DeepFake生成方法合成的假视频来绕过此类探测器。我们进一步证明，我们的对抗性扰动对图像和视频压缩编解码器具有鲁棒性，使其成为现实世界的威胁。我们在白色框和黑盒攻击方案中介绍了管道，可以使基于DNN的DeepFake探测器愚弄假视频为真实。
Recent advances in video manipulation techniques have made the generation of fake videos more accessible than ever before. Manipulated videos can fuel disinformation and reduce trust in media. Therefore detection of fake videos has garnered immense interest in academia and industry. Recently developed Deepfake detection methods rely on deep neural networks (DNNs) to distinguish AI-generated fake videos from real videos. In this work, we demonstrate that it is possible to bypass such detectors by adversarially modifying fake videos synthesized using existing Deepfake generation methods. We further demonstrate that our adversarial perturbations are robust to image and video compression codecs, making them a real-world threat. We present pipelines in both white-box and black-box attack scenarios that can fool DNN based Deepfake detectors into classifying fake videos as real.
