论文标题
Convmixer模型的加密方法而没有性能降解
An Encryption Method of ConvMixer Models without Performance Degradation
论文作者
论文摘要
在本文中，我们提出了一种使用秘密钥匙的Convmixer模型的加密方法。已经研究了DNN模型的加密方法，以实现对抗性防御，模型保护和保护隐私图像分类。但是，与普通模型相比，常规加密方法的使用降低了模型的性能。因此，我们提出了一种新颖的方法来加密交流器模型。该方法是基于Convmixer具有的嵌入体系结构进行的，并且使用该方法加密的模型才能具有与使用秘密钥匙加密的测试图像时使用普通图像训练的模型相同的性能。此外，提出的方法不需要任何特殊准备的数据进行模型培训或网络修改。在实验中，在CIFAR10数据集中的图像分类任务中，根据分类精度和模型保护评估了所提出方法的有效性。
In this paper, we propose an encryption method for ConvMixer models with a secret key. Encryption methods for DNN models have been studied to achieve adversarial defense, model protection and privacy-preserving image classification. However, the use of conventional encryption methods degrades the performance of models compared with that of plain models. Accordingly, we propose a novel method for encrypting ConvMixer models. The method is carried out on the basis of an embedding architecture that ConvMixer has, and models encrypted with the method can have the same performance as models trained with plain images only when using test images encrypted with a secret key. In addition, the proposed method does not require any specially prepared data for model training or network modification. In an experiment, the effectiveness of the proposed method is evaluated in terms of classification accuracy and model protection in an image classification task on the CIFAR10 dataset.
