  We propose a transformation network for generating visually-protected images
for privacy-preserving DNNs. The proposed transformation network is trained by
using a plain image dataset so that plain images are transformed into visually
protected ones. Conventional perceptual encryption methods have a weak
visual-protection performance and some accuracy degradation in image
classification. In contrast, the proposed network enables us not only to
strongly protect visual information but also to maintain the image
classification accuracy that using plain images achieves. In an image
classification experiment, the proposed network is demonstrated to strongly
protect visual information on plain images without any performance degradation
under the use of CIFAR datasets. In addition, it is shown that the visually
protected images are robust against a DNN-based attack, called inverse
transformation network attack (ITN-Attack) in an experiment.
