Recent studies have shown that Convolutional Neural Networks (CNNs) are
vulnerable to a small perturbation of input called "adversarial examples". In
this work, we propose a new feedforward CNN that improves robustness in the
presence of adversarial noise. Our model uses stochastic additive noise added
to the input image and to the CNN models. The proposed model operates in
conjunction with a CNN trained with either standard or adversarial objective
function. In particular, convolution, max-pooling, and ReLU layers are modified
to benefit from the noise model. Our feedforward model is parameterized by only
a mean and variance per pixel which simplifies computations and makes our
method scalable to a deep architecture. From CIFAR-10 and ImageNet test, the
proposed model outperforms other methods and the improvement is more evident
for difficult classification tasks or stronger adversarial noise.

这篇论文提出了一种新的前馈卷积神经网络模型，通过引入随机加性噪音来增强网络对抗噪音的鲁棒性，其模型仅基于每个像素的均值和方差来简化计算，训练效果优于其他方法，特别是对于更难的分类任务或更强的对抗性噪声。