Enhancing Adversarial Attack Defenses for Image Identification Using Generative Adversarial Networks

Deep neural networks are increasingly used in image processing tasks. However, deep learning models often show vulnerability when facing adversarial attacks. Active defense is an important method to deal with adversarial attacks in image identification. This paper proposes an active defense strategy for Generative Adversarial Network (GAN) against adversarial attacks. The proposed method is that when the target network has been trained and remains unchanged, the perturbation generated by the generator is added to the original image and then input to the target network, which has little effect on the performance of the target network and can resist adversarial attacks well. The experimental results of implementing five adversarial attacks on three target network models based on the dataset MNIST and two target network models based on CIFAR10 and comparing them with two defense methods show that our method has achieved good performance in defense effect.