Towards Robust Deep Learning with Ensemble Networks and Noisy Layers
This work addresses adversarial robustness in deep learning for image classification, but it appears incremental as it builds on existing ensemble and noisy layer techniques.
The paper tackles the problem of adversarial examples in deep learning for image classification by combining two mechanisms: one that increases robustness at the expense of accuracy and another that improves accuracy without always increasing robustness, resulting in protection against adversarial attacks while retaining accuracy, with experimental results and a robustness guarantee provided.
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.