  We propose a novel image transformation scheme using generative adversarial
networks (GANs) for privacy-preserving deep neural networks (DNNs). The
proposed scheme enables us not only to apply images without visual information
to DNNs, but also to enhance robustness against ciphertext-only attacks (COAs)
including DNN-based attacks. In this paper, the proposed transformation scheme
is demonstrated to be able to protect visual information on plain images, and
the visually-protected images are directly applied to DNNs for
privacy-preserving image classification. Since the proposed scheme utilizes
GANs, there is no need to manage encryption keys. In an image classification
experiment, we evaluate the effectiveness of the proposed scheme in terms of
classification accuracy and robustness against COAs.
