Although the recent progress is substantial, deep learning methods can be
vulnerable to the maliciously generated adversarial examples. In this paper, we
present a novel training procedure and a thresholding test strategy, towards
robust detection of adversarial examples. In training, we propose to minimize
the reverse cross-entropy (RCE), which encourages a deep network to learn
latent representations that better distinguish adversarial examples from normal
ones. In testing, we propose to use a thresholding strategy as the detector to
filter out adversarial examples for reliable predictions. Our method is simple
to implement using standard algorithms, with little extra training cost
compared to the common cross-entropy minimization. We apply our method to
defend various attacking methods on the widely used MNIST and CIFAR-10
datasets, and achieve significant improvements on robust predictions under all
the threat models in the adversarial setting.

本论文提出一种新颖的训练方法和阈值测试策略，以实现对恶意制造的对抗样本的鲁棒检测。在训练中，我们提出了最小化反交叉熵（RCE）的方法，以鼓励深度网络学习更好地区分对抗样本和正常样本的潜在表示。在测试中，我们提出了使用阈值策略作为检测器，以过滤出对抗样本，以实现可靠预测，我们的方法使用标准算法简单实现，与常见的交叉熵最小化相比，几乎没有额外的训练成本。我们将该方法应用于广泛使用的 MNIST 和 CIFAR-10 数据集上的各种攻击方法，并在对抗环境下的所有威胁模型中实现显著的鲁棒预测改进。