论文标题
一种有效的融合方法来增强CNN的鲁棒性
An Effective Fusion Method to Enhance the Robustness of CNN
论文作者
论文摘要
随着技术的发展，卷积神经网络的应用改善了我们生活的便利。但是，在图像分类字段中，已经发现，当将某些扰动添加到图像中时，CNN会将其错误分类。因此，已经提出了各种防御方法。先前的方法仅考虑了如何将模块合并到网络中以提高鲁棒性，但并未关注模块合并的方式。在本文中，我们设计了一种新的融合方法来增强CNN的鲁棒性。我们使用基于DOT产品的方法将Denoising模块添加到RESNET18和注意机制，以进一步提高模型的鲁棒性。 CIFAR10上的实验结果表明，我们的方法比FGSM和PGD攻击下的最新方法更好，并且更好。
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.
