LRCM: Enhancing Adversarial Purification through Latent Representation Compression

Abstract In the current context of the extensive use of deep neural networks, it has been observed that neural network models are vulnerable to adversarial perturbations, which may lead to unexpected results. In this paper, we introduce an Adversarial Purification Model rooted in latent representation compression, aimed at enhancing the robustness of deep learning models. Initially, we employ an encoder-decoder architecture inspired by the U-net to extract features from input samples. Subsequently, these features undergo a process of information compression to remove adversarial perturbations from the latent space. To counteract the model's tendency to overly focus on fine-grained details of input samples, resulting in ineffective adversarial sample purification, an early freezing mechanism is introduced during the encoder training process. We conducted experiments using the proposed model to purify adversarial samples generated by various methods using CIFAR-10 and ImageNet datasets. The experimental results revealed a notable increase in accuracy when employing the ResNet classifier.