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

An Encryption Method of ConvMixer Models without Performance Degradation