论文标题
通过合奏网络和嘈杂的层进行强大的深度学习
Towards Robust Deep Learning with Ensemble Networks and Noisy Layers
论文作者
论文摘要
在本文中，我们提供了一种深入学习的方法，可以防止图像分类型网络中的对抗示例。该方法取决于两种机制：1）一种以牺牲准确性为代价的机制，以及2）提高准确性但并不总是会提高鲁棒性的机制。我们表明，结合两种机制的方法可以在保留准确性的同时为对抗性例子提供保护。我们通过实验结果对我们的方法进行潜在的攻击，以证明其有效性。我们还为我们的方法提供了坚固的保证，以及对保证的解释。
In this paper we provide an approach for deep learning that protects against adversarial examples in image classification-type networks. The approach relies on two mechanisms:1) a mechanism that increases robustness at the expense of accuracy, and, 2) a mechanism that improves accuracy but does not always increase robustness. We show that an approach combining the two mechanisms can provide protection against adversarial examples while retaining accuracy. We formulate potential attacks on our approach with experimental results to demonstrate its effectiveness. We also provide a robustness guarantee for our approach along with an interpretation for the guarantee.
