Reconstructing images with attention generative adversarial network against adversarial attacks

Abstract

Deep learning is widely used in the field of computer vision, but the emergence of adversarial examples threatens its application. How to effectively detect adversarial examples and correct their labels has become a problem to be solved in this application field. Generative adversarial networks (GANs) can effectively learn the features from images. Based on GAN, this work proposes a defense method called “Reconstructing images with GAN” (RIG). The adversarial examples are generated by attack algorithms reconstructed by the trained generator of RIG to eliminate the perturbations of the adversarial examples, which disturb the models for classification, so that the models can restore their labels when classifying the reconstructed images. In addition, to improve the defensive performance of RIG, the attention mechanism (AM) is introduced to enhance the defense effect of RIG, which is called reconstructing images with attention GAN (RIAG). Experiments show that RIG and RIAG can effectively eliminate the perturbations of the adversarial examples. The results also show that RIAG has a better defensive performance than RIG in eliminating the perturbations of adversarial examples, which indicates that the introduction of AM can effectively improve the defense effect of RIG.