LRCM: Enhancing Adversarial Purification Through Latent Representation Compression

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 tested our model's ability to purify adversarial samples generated from the CIFAR-10, CIFAR-100, and ImageNet datasets using various methods. These samples were then used to test ResNet, an image recognition classifiers. Our experiments covered different resolutions and attack types to fully assess LRCM's effectiveness against adversarial attacks. We also compared LRCM with other defence strategies, demonstrating its strong defensive capabilities.