We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
基于SmsGAN的对抗样本修复.
- Authors
赵俊杰; 王金伟
- Abstract
Due to adversarial examples7 serious interference to the detection models based on deep learning,a recovery method of adversarial examples based on stochastic multifilter statistical generative adversarial network (SmsGAN) was proposed in this work. To achieve high-precision forensics of adversarial examples,this paper proposed the feature statistical layer in the stochastic multifilter statistical network (SmsNet) . The feature map output from each convolution layer was directly transferred to the feature statistical layer to get global feature values. Stochastic multifilter statistical generative adversarial network (SmsGAN) used SmsNet as its discriminator ? and its generator used a multi-scale convolution kernel parallel structure to avoid checkerboard artifacts. The generator's loss function consisted of two parts,discriminative loss and guidance loss,to form a target guidance generator. The adversarial examples entered the down-sampling network to obtain local statistical features ? and then these features were sent into SmsGAN for reconstruction to get denoised examples* Using Sms-GAN to recover the adversarial examples ? the recovery rate reached 91. 3%,and the average PSNR reached more than 32. The visual quality was better than the traditional signal processing method,and the purpose of removing the anti-disturbance was achieved*.
- Publication
Journal of Zhengzhou University: Engineering Science, 2021, Vol 42, Issue 1, p50
- ISSN
1671-6833
- Publication type
Article
- DOI
10.13705/j.issn.1671-6833.2021.01.008