  Various visual information protection methods have been proposed for
privacy-preserving deep neural networks (DNNs). In contrast, attack methods on
such protection methods have been studied simultaneously. In this paper, we
evaluate state-of-the-art visual protection methods for privacy-preserving DNNs
in terms of visual security against ciphertext-only attacks (COAs). We focus on
brute-force attack, feature reconstruction attack (FR-Attack), inverse
transformation attack (ITN-Attack), and GAN-based attack (GAN-Attack), which
have been proposed to reconstruct visual information on plain images from the
visually-protected images. The detail of various attack is first summarized,
and then visual security of the protection methods is evaluated. Experimental
results demonstrate that most of protection methods, including pixel-wise
encryption, have not enough robustness against GAN-Attack, while a few
protection methods are robust enough against GAN-Attack.
