Feedback Learning for Improving the Robustness of Neural Networks

Chang Song; Zuoguan Wang; Hai Li

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arXiv · 1909.05443

Feedback Learning for Improving the Robustness of Neural Networks

Chang Song·Zuoguan Wang·Hai Li

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Abstract

Recent research studies revealed that neural networks are vulnerable to adversarial attacks. State-of-the-art defensive techniques add various adversarial examples in training to improve models' adversarial robustness. However, these methods are not universal and can't defend unknown or non-adversarial evasion attacks. In this paper, we analyze the model robustness in the decision space. A feedback learning method is then proposed, to understand how well a model learns and to facilitate the retraining process of remedying the defects. The evaluations according to a set of distance-based criteria show that our method can significantly improve models' accuracy and robustness against different types of evasion attacks. Moreover, we observe the existence of inter-class inequality and propose to compensate it by changing the proportions of examples generated in different classes.

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BibTeX RIS

Chang Song, Zuoguan Wang, Hai Li. 2019-09-12. Feedback Learning for Improving the Robustness of Neural Networks. https://arxiv.org/abs/1909.05443

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