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.
翻译:在本文中,我们提出使用ConvMixer结构下的加密图像使用隐私保护图像分类方法。 用于隐私保护图像分类任务的有条不紊的乱动图像已经用于保护隐私图像分类任务,但需要同时使用分类网络和适应网络来减少图像加密的影响。 但是,由于适应网络有如此多的参数,大型图像无法应用于常规方法,因为适应网络有如此多的参数。 因此,我们提出了一种新颖的方法,不仅使我们能够在没有适应网络的情况下对ConvMixer使用有条不紊乱的乱动图像进行培训和测试,而且还可以提供比常规方法更高的分类准确性。