Deep neural networks have been found vulnerable to noises like adversarial examples and corruption in practice. A number of adversarial defense methods have been developed, which indeed improve the model robustness towards adversarial examples in practice. However, only relying on training with the data mixed with noises, most of them still fail to defend the generalized types of noises. Motivated by the fact that hidden layers play a very important role in maintaining a robust model, this paper comes up with a simple yet powerful training algorithm named Adversarial Noise Propagation (ANP) that injects diversified noises into the hidden layers in a layer-wise manner. We show that ANP can be efficiently implemented by exploiting the nature of the popular backward-forward training style for deep models. To comprehensively understand the behaviors and contributions of hidden layers, we further explore the insights from hidden representation insensitivity and human vision perception alignment. Extensive experiments on MNIST, CIFAR-10, CIFAR-10-C, CIFAR-10-P and ImageNet demonstrate that ANP enables the strong robustness for deep models against the generalized noises including both adversarial and corrupted ones, and significantly outperforms various adversarial defense methods.

本篇论文提出了一种名为 Adversarial Noise Propagation 的训练算法，将噪声注入隐藏层以提高深度模型的鲁棒性，并证明不同的隐藏层对模型鲁棒性和整洁的准确性有不同的贡献。该算法可有效地与其他对抗性训练方法相结合，进一步提高模型的鲁棒性。在 MNIST、CIFAR-10、CIFAR-10-C、CIFAR-10-P 和 ImageNet 上的广泛实验表明，ANP 实现了深度模型对对抗性和受损数据均具有强大的鲁棒性，并明显优于各种对抗性防御方法。

通过对抗性噪声传播训练强大的深度神经网络