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.

通过分析决策空间中的模型鲁棒性，提出一种反馈学习方法，以了解模型的学习情况，促进纠正缺陷的重新训练过程。根据一组基于距离的准则进行的评估表明，我们的方法可以显著提高模型的准确性和对各种逃逸攻击的鲁棒性，同时观察到跨类不平等的存在，并提出通过改变不同类别中生成的示例的比例来弥补它。

神经网络鲁棒性的反馈学习