论文标题
黑盒认证和在对抗扰动下学习
Black-box Certification and Learning under Adversarial Perturbations
论文作者
论文摘要
我们从学习者的角度正式研究了对抗性扰动下的分类问题，以及旨在证明给定黑盒分类器的鲁棒性的第三方。我们分析了半监督学习的PAC型框架，并确定在这种情况下正确学习VC类的可能性和可能性结果。我们进一步在有限的查询预算下推出了新的黑盒认证设置，并为各种类别的预测指标和扰动分析。我们还考虑了旨在寻找对抗性示例的黑盒对手的观点，表明存在具有多项式查询复杂性的对手可以暗示存在样本有效的稳健学习者。
We formally study the problem of classification under adversarial perturbations from a learner's perspective as well as a third-party who aims at certifying the robustness of a given black-box classifier. We analyze a PAC-type framework of semi-supervised learning and identify possibility and impossibility results for proper learning of VC-classes in this setting. We further introduce a new setting of black-box certification under limited query budget, and analyze this for various classes of predictors and perturbation. We also consider the viewpoint of a black-box adversary that aims at finding adversarial examples, showing that the existence of an adversary with polynomial query complexity can imply the existence of a sample efficient robust learner.
