In this paper, we propose a combined use of transformed images and vision transformer (ViT) models transformed with a secret key. We show for the first time that models trained with plain images can be directly transformed to models trained with encrypted images on the basis of the ViT architecture, and the performance of the transformed models is the same as models trained with plain images when using test images encrypted with the key. In addition, the proposed scheme does not require any specially prepared data for training models or network modification, so it also allows us to easily update the secret key. In an experiment, the effectiveness of the proposed scheme is evaluated in terms of performance degradation and model protection performance in an image classification task on the CIFAR-10 dataset.

翻译：在本文中,我们提议合并使用经过秘密钥匙转换的变形图像和视觉变压器模型。我们第一次显示,经过简单图像培训的模型可以直接转换成根据ViT结构以加密图像培训的模型,而经过改造的模型的性能与使用用用钥匙加密的测试图像培训的普通图像的模型相同。此外,拟议的方案不需要任何特别准备的数据用于培训模型或网络修改,因此也使我们能够方便地更新秘密钥匙。在一次实验中,在CIFAR-10数据集的图像分类任务中,从性能退化和模型保护性能的角度对拟议方案的有效性进行了评估。