Adversarial Example Generation Algorithm Based on Transformer and GAN
Adversarial attack and defense is a popular research area in computer security.Trans-GAN,an adversarial example generation algorithm based on the combination of Transformer and Generate Adversarial Network(GAN),is proposed to address the problems of the poor visual quality of existing gradient-based adversarial example generation methods and the low generation efficiency of optimization-based methods.First,the algorithm utilizes the powerful visual representation capability of the Transformer as a reconstruction network for receiving clean images and generating adversarial noise.Second,the Transformer reconstruction network is combined with a deep convolutional network-based discriminator as a generator to form a GAN architecture,which improves the authenticity of the generated images and ensures the stability of training.Meanwhile,the improved attention mechanism,Targeted Self-Attention,is proposed to introduce target labels as a priori knowledge when training the network,which guides the network model to learn to generate adversarial perturbations with specific attack targets.Finally,adversarial noise is added to the clean examples using skip-connections to form adversarial examples.Experimental results demonstrate that the proposed algorithm achieves an attack success rate of more than 99.9% on both models used for the MNIST dataset and 96.36% and 98.47% on the two models used for the CIFAR10 dataset,outperforming the current state-of-the-art generative-based adversarial attack methods.The qualitative results show that compared to the Fast Gradient Sign Method(FGSM)and Projected Gradient Descent(PGD)algorithms,the generated adversarial noise of the Trans-GAN algorithm is less perturbed,and the formed adversarial examples are more natural and meet the requirements of human vision,which is not easily distinguished.
deep neural networkadversarial exampleadversarial attackTransformer modelGenerate Adversarial Network(GAN)attention mechanism
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