  In this paper, we propose a privacy-preserving image classification method
that uses encrypted images and an isotropic network such as the vision
transformer. The proposed method allows us not only to apply images without
visual information to deep neural networks (DNNs) for both training and testing
but also to maintain a high classification accuracy. In addition, compressible
encrypted images, called encryption-then-compression (EtC) images, can be used
for both training and testing without any adaptation network. Previously, to
classify EtC images, an adaptation network was required before a classification
network, so methods with an adaptation network have been only tested on small
images. To the best of our knowledge, previous privacy-preserving image
classification methods have never considered image compressibility and patch
embedding-based isotropic networks. In an experiment, the proposed
privacy-preserving image classification was demonstrated to outperform
state-of-the-art methods even when EtC images were used in terms of
classification accuracy and robustness against various attacks under the use of
two isotropic networks: vision transformer and ConvMixer.
