  We propose a novel method for privacy-preserving deep neural networks (DNNs)
with the Vision Transformer (ViT). The method allows us not only to train
models and test with visually protected images but to also avoid the
performance degradation caused from the use of encrypted images, whereas
conventional methods cannot avoid the influence of image encryption. A domain
adaptation method is used to efficiently fine-tune ViT with encrypted images.
In experiments, the method is demonstrated to outperform conventional methods
in an image classification task on the CIFAR-10 and ImageNet datasets in terms
of classification accuracy.
