Privacy-Preserving Image Classification Using ConvMixer with Adaptative Permutation Matrix and Block-Wise Scrambled Image Encryption

In this paper, we propose a privacy-preserving image classification method using block-wise scrambled images and a modified ConvMixer. Conventional block-wise scrambled encryption methods usually need the combined use of an adaptation network and a classifier to reduce the influence of image encryption. However, we point out that it is problematic to utilize large-size images with conventional methods using an adaptation network because of the significant increment in computation cost. Thus, we propose a novel privacy-preserving method that allows us not only to apply block-wise scrambled images to ConvMixer for both training and testing without an adaptation network, but also to provide a high classification accuracy and strong robustness against attack methods. Furthermore, we also evaluate the computation cost of state-of-the-art privacy-preserving DNNs to confirm that our proposed method requires fewer computational resources. In an experiment, we evaluated the classification performance of the proposed method on CIFAR-10 and ImageNet compared with other methods and the robustness against various ciphertext-only-attacks.