论文标题
堆叠和包的坚固方法：当地的Lipschitz方式
The robust way to stack and bag: the local Lipschitz way
论文作者
论文摘要
最近的研究表明，神经网络的当地Lipschitz常数直接影响其对抗性鲁棒性。我们利用这种关系来构建神经网络的整体，不仅可以提高准确性，而且还提供了增强的对抗性鲁棒性。派生了两种不同的合奏方法的本地Lipschitz常数 - 包装和堆叠 - 最适合确保对抗性鲁棒性。在存在白色框攻击，FGSM和PGD的情况下，在MNIST和CIFAR-10数据集上测试了所提出的集合体系结构。发现所提出的架构比a）单个网络和b）传统的集合方法更强大。
Recent research has established that the local Lipschitz constant of a neural network directly influences its adversarial robustness. We exploit this relationship to construct an ensemble of neural networks which not only improves the accuracy, but also provides increased adversarial robustness. The local Lipschitz constants for two different ensemble methods - bagging and stacking - are derived and the architectures best suited for ensuring adversarial robustness are deduced. The proposed ensemble architectures are tested on MNIST and CIFAR-10 datasets in the presence of white-box attacks, FGSM and PGD. The proposed architecture is found to be more robust than a) a single network and b) traditional ensemble methods.
