  With the development of technology rapidly, applications of convolutional
neural networks have improved the convenience of our life. However, in image
classification field, it has been found that when some perturbations are added
to images, the CNN would misclassify it. Thus various defense methods have been
proposed. The previous approach only considered how to incorporate modules in
the network to improve robustness, but did not focus on the way the modules
were incorporated. In this paper, we design a new fusion method to enhance the
robustness of CNN. We use a dot product-based approach to add the denoising
module to ResNet18 and the attention mechanism to further improve the
robustness of the model. The experimental results on CIFAR10 have shown that
our method is effective and better than the state-of-the-art methods under the
attack of FGSM and PGD.
