标题： 针对成本敏感分类器的对抗样本 显示英文标题

标题： Adversarial Examples for Cost-Sensitive Classifiers

摘要： 鉴于安全关键分类问题，我们研究针对成本敏感分类器的对抗攻击。 我们使用当前最先进的对抗性防御神经网络分类器[1]作为基础模型。 然后通过网络前向评估中的最终处理步骤实现成本敏感预测。 我们评估了成本敏感分类器对各种攻击的有效性，并引入了一种新的成本敏感攻击，在某些情况下表现优于目标攻击。 我们还探讨了防御者可以采取的措施，以限制其对这些攻击的易受性。 这种攻击者/防御者场景自然被表述为一个两人零和有限博弈，我们使用博弈论对其进行分析。 显示英文摘要

摘要： Motivated by safety-critical classification problems, we investigate adversarial attacks against cost-sensitive classifiers. We use current state-of-the-art adversarially-resistant neural network classifiers [1] as the underlying models. Cost-sensitive predictions are then achieved via a final processing step in the feed-forward evaluation of the network. We evaluate the effectiveness of cost-sensitive classifiers against a variety of attacks and we introduce a new cost-sensitive attack which performs better than targeted attacks in some cases. We also explored the measures a defender can take in order to limit their vulnerability to these attacks. This attacker/defender scenario is naturally framed as a two-player zero-sum finite game which we analyze using game theory.