论文标题
建筑对抗鲁棒性：深度追求的案例
Architectural Adversarial Robustness: The Case for Deep Pursuit
论文作者
论文摘要
尽管具有无与伦比的性能，但深层神经网络仍然容易受到几乎无法察觉的对抗噪声的靶向攻击。尽管尚不清楚这种灵敏度的根本原因，但可以通过将馈送网络的每一层重新标记为稀疏编码问题的近似解决方案来简化理论分析。从理论上讲，使用基础追求的迭代解决方案更加稳定，并改善了对抗性鲁棒性。但是，级联的层面追踪实现遭受了更深的网络中错误积累的影响。相比之下，我们的新方法的新方法将所有层的激活近似为单个全局优化问题，从而使我们可以考虑具有跳过连接（例如残留网络）的更深层的真实世界体系结构。在实验上，我们的方法证明了对对抗噪声的鲁棒性。
Despite their unmatched performance, deep neural networks remain susceptible to targeted attacks by nearly imperceptible levels of adversarial noise. While the underlying cause of this sensitivity is not well understood, theoretical analyses can be simplified by reframing each layer of a feed-forward network as an approximate solution to a sparse coding problem. Iterative solutions using basis pursuit are theoretically more stable and have improved adversarial robustness. However, cascading layer-wise pursuit implementations suffer from error accumulation in deeper networks. In contrast, our new method of deep pursuit approximates the activations of all layers as a single global optimization problem, allowing us to consider deeper, real-world architectures with skip connections such as residual networks. Experimentally, our approach demonstrates improved robustness to adversarial noise.
