  This article presents block-wise image encryption for the vision transformer
and its applications. Perceptual image encryption for deep learning enables us
not only to protect the visual information of plain images but to also embed
unique features controlled with a key into images and models. However, when
using conventional perceptual encryption methods, the performance of models is
degraded due to the influence of encryption. In this paper, we focus on
block-wise encryption for the vision transformer, and we introduce three
applications: privacy-preserving image classification, access control, and the
combined use of federated learning and encrypted images. Our scheme can have
the same performance as models without any encryption, and it does not require
any network modification. It also allows us to easily update the secret key. In
experiments, the effectiveness of the scheme is demonstrated in terms of
performance degradation and access control on the CIFAR10 and CIFAR-100
datasets.
