论文标题
使用自适应置换矩阵的Convmixer使用Convmixer进行隐私的图像分类
Privacy-Preserving Image Classification Using ConvMixer with Adaptive Permutation Matrix
论文作者
论文摘要
在本文中，我们在使用Convmixer结构下使用加密图像提出了一种隐私图像分类方法。宽阔的炒图像对各种攻击都足够强大，已用于保护隐私图像分类任务，但是需要组合使用分类网络和适应网络来减少图像加密的影响。但是，由于适应网络具有许多参数，因此无法将大尺寸的图像应用于传统方法。因此，我们提出了一种新颖的方法，该方法不仅使我们不仅可以在没有适应网络的情况下应用块炒图像进行训练和测试，而且还提供了比常规方法更高的分类精度。
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.
