  In this paper, we propose an encryption method for ConvMixer models with a
secret key. Encryption methods for DNN models have been studied to achieve
adversarial defense, model protection and privacy-preserving image
classification. However, the use of conventional encryption methods degrades
the performance of models compared with that of plain models. Accordingly, we
propose a novel method for encrypting ConvMixer models. The method is carried
out on the basis of an embedding architecture that ConvMixer has, and models
encrypted with the method can have the same performance as models trained with
plain images only when using test images encrypted with a secret key. In
addition, the proposed method does not require any specially prepared data for
model training or network modification. In an experiment, the effectiveness of
the proposed method is evaluated in terms of classification accuracy and model
protection in an image classification task on the CIFAR10 dataset.
