论文标题
迈向对抗分类的一致性
Towards Consistency in Adversarial Classification
论文作者
论文摘要
在本文中，我们研究了对抗性例子的一致性问题。具体来说，我们解决了以下问题：是否可以将替代损失用作最小化$ 0/1 $损失的代理，而在存在测试时间的对手的情况下会改变投入？与标准分类任务不同，这个问题不能简化为点最小化问题，并且校准不足以确保一致性。在本文中，我们揭示了对对抗性问题特有的一些病理行为，并表明在这种情况下，凸面替代损失无法保持一致或校准。因此，有必要设计其他可以用于解决对抗性一致性问题的替代功能。作为设计这样的类别的第一步，我们确定了在对抗和标准设置中校准的替代损失的足够和必要条件。最后，我们为建立一类损失提供了一些指示，这些损失可能在对抗框架中是一致的。
In this paper, we study the problem of consistency in the context of adversarial examples. Specifically, we tackle the following question: can surrogate losses still be used as a proxy for minimizing the $0/1$ loss in the presence of an adversary that alters the inputs at test-time? Different from the standard classification task, this question cannot be reduced to a point-wise minimization problem, and calibration needs not to be sufficient to ensure consistency. In this paper, we expose some pathological behaviors specific to the adversarial problem, and show that no convex surrogate loss can be consistent or calibrated in this context. It is therefore necessary to design another class of surrogate functions that can be used to solve the adversarial consistency issue. As a first step towards designing such a class, we identify sufficient and necessary conditions for a surrogate loss to be calibrated in both the adversarial and standard settings. Finally, we give some directions for building a class of losses that could be consistent in the adversarial framework.
