# Privacy-Preserving Image Classification Using ConvMixer with Adaptive Permutation Matrix Source: [Privacy-Preserving Image Classification Using ConvMixer with Adaptive Permutation Matrix]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:28:36.143Z Authors: Zheng Qi, AprilPyone MaungMaung, Hitoshi Kiya Published: 2022-08-04T09:55:31.000Z ## Abstract In this paper, we propose a privacy-preserving image classification method using encrypted images under the use of the ConvMixer structure. Block-wise scrambled images, which are robust enough against various attacks, have been used for privacy-preserving image classification tasks, but the combined use of a classification network and an adaptation network is needed to reduce the influence of image encryption. However, images with a large size cannot be applied to the conventional method with an adaptation network because the adaptation network has so many parameters. Accordingly, we propose a novel method, which allows us not only to apply block-wise scrambled images to ConvMixer for both training and testing without the adaptation network, but also to provide a higher classification accuracy than conventional methods. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()