Fortified Networks：通过对隐藏表示的流形进行建模来提高深度网络的鲁棒性
Fortified Networks: Improving the Robustness of Deep Networks by Modeling the Manifold of Hidden Representations
摘要：
深度网络在各种重要任务中取得了令人印象深刻的成果。然而，一个已知的弱点是，当对与训练分布不同的数据进行评估时，即使这些差异非常小，也无法表现良好，就像对抗性示例一样。我们提出了强化网络，这是对现有网络的一种简单转换，它通过识别隐藏状态何时脱离数据流形来强化深层网络中的隐藏层，并将这些隐藏状态映射回网络性能良好的数据流形部分。我们的主要贡献是证明强化这些隐藏状态可以提高深度网络的鲁棒性，我们的实验（i）证明了在黑盒和白盒威胁模型中对标准对抗性攻击的鲁棒性有所提高;（ii）表明我们的改进主要不是由于梯度掩蔽问题，以及（iii）显示了在隐藏层而不是输入空间中进行这种强化的优势。
英文摘要：
Deep networks have achieved impressive results across a variety of important tasks. However a known weakness is a failure to perform well when evaluated on data which differ from the training distribution, even if these differences are very small, as is the case with adversarial examples. We propose Fortified Networks, a simple transformation of existing networks, which fortifies the hidden layers in a deep network by identifying when the hidden states are off of the data manifold, and maps these hidden states back to parts of the data manifold where the network performs well. Our principal contribution is to show that fortifying these hidden states improves the robustness of deep networks and our experiments (i) demonstrate improved robustness to standard adversarial attacks in both black-box and white-box threat models; (ii) suggest that our improvements are not primarily due to the gradient masking problem and (iii) show the advantage of doing this fortification in the hidden layers instead of the input space.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
