论文标题
基于式编码器的攻击，用于障碍物的面部图像
StyleGAN Encoder-Based Attack for Block Scrambled Face Images
论文作者
论文摘要
在本文中，我们提出了一种攻击方法，以阻止炒面的面部图像，尤其是加密，然后是使用现有的强大式式式编码器和解码器，然后使用了施加的图像。我们专注于恢复可以从加密图像中揭示可识别信息的样式，而不是从加密图像中重建相同的图像。所提出的方法通过使用特定的训练策略使用普通和加密的图像对来训练编码器。尽管最新的攻击方法无法从ETC图像中恢复任何感知信息，但该建议的方法揭示了个人识别信息，例如头发颜色，肤色，眼镜，性别，性别等。在Celeba数据集中进行了实验，结果表明，与纯图像相比，重建的图像具有一些感知的相似性。
In this paper, we propose an attack method to block scrambled face images, particularly Encryption-then-Compression (EtC) applied images by utilizing the existing powerful StyleGAN encoder and decoder for the first time. Instead of reconstructing identical images as plain ones from encrypted images, we focus on recovering styles that can reveal identifiable information from the encrypted images. The proposed method trains an encoder by using plain and encrypted image pairs with a particular training strategy. While state-of-the-art attack methods cannot recover any perceptual information from EtC images, the proposed method discloses personally identifiable information such as hair color, skin color, eyeglasses, gender, etc. Experiments were carried out on the CelebA dataset, and results show that reconstructed images have some perceptual similarities compared to plain images.
