  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.
