An adversarial defense algorithm based on robust U-net

Due to the continuous development of neural network technology, it has been widely applied in fields such as autonomous driving and biomedicine. However, adversarial attacks can significantly degrade the performance and reliability of neural networks, making defending against such attacks a crucial research area. In this paper, we propose a robust U-Net and adversarial generative network-based defense method, training the target model on a clean training set without adversarial samples. Firstly, we train the target neural network on a clean training set. Then, we train the robust U-Net using the clean training set, employing reparameterization and random noise to resist adversarial perturbations. To supervise the quality of transformed images, we employ the adversarial generative network, utilizing the RU-Net as the generator and a discriminator to ensure the quality of generated images. Finally, we use the transformed images to retrain the target neural network, obtaining a robust neural network model capable of defending against adversarial attacks. Experimental evaluations on CIFAR-10 and Tiny Image Net demonstrate the effectiveness of our method in countering adversarial attacks and enhancing neural network robustness.