  In this paper, we propose a privacy-preserving image classification method
that is based on the combined use of encrypted images and the vision
transformer (ViT). The proposed method allows us not only to apply images
without visual information to ViT models for both training and testing but to
also maintain a high classification accuracy. ViT utilizes patch embedding and
position embedding for image patches, so this architecture is shown to reduce
the influence of block-wise image transformation. In an experiment, the
proposed method for privacy-preserving image classification is demonstrated to
outperform state-of-the-art methods in terms of classification accuracy and
robustness against various attacks.
