The rapid development of deep neural networks has led to significant success in fields such as computer vision and natural language processing.However,adversarial attacks may inhibit the performance of neural networks,posing a serious threat to the security and confidentiality of various systems.Existing black-box attack methods perform poorly in facial recognition,with a low success rate and low transferability of generated adversarial samples.To this end,a G-MASK adversarial attack method combining Gaussian filtering and mask is proposed.Using the heat map output by Grad-CAM to determine the mask area of adversarial samples,the mask area is perturbed to improve the success rate of black-box attacks.The perturbation integration method is used to improve the black-box migration ability and enhance attack robustness.Gaussian smoothing is applied to the generated perturbations to reduce the difference in interference noise between integrated models,improve image quality,and enhance disturbance masking.Experimental results show that for different facial recognition models,the G-MASK method significantly improves the effectiveness of black-box attacks while ensuring a high success rate of white-box attacks and a better masking ability.Following model perturbation integration,the success rate of white-box attacks on adversarial samples exceeds 98.5%,while the success rate of black-box attacks reaches 75.9%,which is consistent with the fast gradient sign method.Compared with Fast Gradient Symbolic Method(FGSM),Iteration-Fast Gradient Symbolic Method(I-FGSM),Moenttum Iteration-Fast Gradient Symbolic Method(MI-FGSM)yields average improvements of 12.1,10.6,and 8.2 percentage points.
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