In recent years, deep neural networks (DNNs) trained with transformed data have been applied to various applications such as privacy-preserving learning, access control, and adversarial defenses. However, the use of transformed data decreases the performance of models. Accordingly, in this paper, we propose a novel method for fine-tuning models with transformed images under the use of the vision transformer (ViT). The proposed domain adaptation method does not cause the accuracy degradation of models, and it is carried out on the basis of the embedding structure of ViT. In experiments, we confirmed that the proposed method prevents accuracy degradation even when using encrypted images with the CIFAR-10 and CIFAR-100 datasets.

翻译：近年来，基于变换数据训练的深度神经网络已广泛应用于隐私保护学习、访问控制及对抗防御等场景。然而，使用变换数据会降低模型性能。为此，本文提出一种基于视觉Transformer的加密图像微调新方法。所提出的域适应方法不会导致模型精度下降，其实现机制建立在ViT的嵌入结构基础上。实验表明，在CIFAR-10和CIFAR-100数据集上使用加密图像时，该方法有效防止了精度退化。