Abstract: Although deep neural networks perform well in many areas,research shows that deep neural networks are vulnerable to attacks from adversarial examples.There are many algorithms for attacking neural networks,but the attack speed of most attack algorithms is slow.Therefore,the rapid generation of adversarial examples has gradually become the focus of research in the area of adversarial examples.AdvGAN is an algorithm that uses the network to attack another network,which can generate adversarial samples extremely faster than other methods.However,when carrying out a targeted attack,AdvGAN needs to train a network for each target,so the efficiency of the attack is low.In this article,we propose a multi-target attack network(MTA) based on the generative adversarial network,which can complete multi-target attacks and quickly generate adversarial examples by training only once.Experiments show that MTA has a higher success rate for targeted attacks on the CIFAR10 and MNIST datasets than AdvGAN.We have also done adversarial sample transfer experiments and attack experiments under defense.The results show that the transferability of the adversarial examples generated by MTA is stronger than other multi-target attack algorithms,and our MTA method also has a higher attack success rate under defense.

Key words: Adversarial attack, Adversarial example, Generative adversarial network, Multi-target attack, Neural network