3 accepted papers
A new model for adversarial robustness was introduced by Goldwasser et al. in [GKKM20]. In this model the authors present a selective and transductive learning algorithm which guarantees a low test error and low rejection rate wrt to the original distribution. Moreover, a lower bound in terms of the…
There are two main attack models considered in the adversarial robustness literature: black-box and white-box. We consider these threat models as two ends of a fine-grained spectrum, indexed by the number of queries the adversary can ask. Using this point of view we investigate how many queries the…
Modern machine learning models with very high accuracy have been shown to be vulnerable to small, adversarially chosen perturbations of the input. Given black-box access to a high-accuracy classifier f, we show how to construct a new classifier g that has high accuracy and is also robust to adversar…