Recent advances in autoencoders and generative models have given rise to effective video forgery methods, used for generating so-called "deepfakes". Mitigation research is mostly focused on post-factum deepfake detection and not on prevention. We complement these efforts by introducing a novel class of adversarial attacks---training-resistant attacks---which can disrupt face-swapping autoencoders whether or not its adversarial images have been included in the training set of said autoencoders. We propose the Oscillating GAN (OGAN) attack, a novel attack optimized to be training-resistant, which introduces spatial-temporal distortions to the output of face-swapping autoencoders. To implement OGAN, we construct a bilevel optimization problem, where we train a generator and a face-swapping model instance against each other. Specifically, we pair each input image with a target distortion, and feed them into a generator that produces an adversarial image. This image will exhibit the distortion when a face-swapping autoencoder is applied to it. We solve the optimization problem by training the generator and the face-swapping model simultaneously using an iterative process of alternating optimization. Next, we analyze the previously published Distorting Attack and show it is training-resistant, though it is outperformed by our suggested OGAN. Finally, we validate both attacks using a popular implementation of FaceSwap, and show that they transfer across different target models and target faces, including faces the adversarial attacks were not trained on. More broadly, these results demonstrate the existence of training-resistant adversarial attacks, potentially applicable to a wide range of domains.
翻译:自动校正和基因模型的最近进步产生了有效的视频伪造方法,用于制作所谓的“深假” 。 减缓研究主要侧重于事后的深假检测而不是预防。 我们通过引入新型的对抗性攻击 — — 训练抗冲击性攻击 — — 能够干扰对面模拟自动校正图像,而不管其对面图像是否包含在所述自动校正者的培训组合中。 我们提议对准GAN(OGAN)攻击,这是新颖的、最能适应培训的打击,它主要侧重于对面反面的检测而不是预防。 为了实施OGAN,我们构建了一个双级优化问题,在那里我们训练一个发动机和对面反面的模型,我们将每个输入图像配对成一个目标扭曲,并将它们输入到一个能产生对面图像的发电机中。当对面自动校正图像应用时,新颖的攻击将呈现扭曲的面面部面部面部面部面部面部面部面部的扭曲,我们同时通过升级的模拟培训来解决这些对面攻击的升级问题, 最后,我们通过升级的升级的训练来展示了对面部和升级的模型。