Images posted online present a privacy concern in that they may be used as reference examples for a facial recognition system. Such abuse of images is in violation of privacy rights but is difficult to counter. It is well established that adversarial example images can be created for recognition systems which are based on deep neural networks. These adversarial examples can be used to disrupt the utility of the images as reference examples or training data. In this work we use a Generative Adversarial Network (GAN) to create adversarial examples to deceive facial recognition and we achieve an acceptable success rate in fooling the face recognition. Our results reduce the training time for the GAN by removing the discriminator component. Furthermore, our results show knowledge distillation can be employed to drastically reduce the size of the resulting model without impacting performance indicating that our contribution could run comfortably on a smartphone

翻译：在线上张贴的图像具有隐私问题,因为这些图像可以用作面部识别系统的参考示例。这种滥用图像的行为侵犯了隐私权,但很难予以反驳。众所周知,可以为基于深神经网络的识别系统创建对抗性实例图像。这些对抗性实例可以用来破坏图像作为参考示例或培训数据的效用。在这项工作中,我们使用创性对立网络(GAN)来创建对抗性实例来欺骗面部识别,我们在欺骗面部识别中取得了可接受的成功率。我们的成果通过消除歧视成分减少了GAN的培训时间。此外,我们的成果显示,知识蒸馏可以大幅缩小所生成模型的规模,而不会影响功能,表明我们的贡献可以在智能手机上顺利运行。