(Blog post written by Xiao Zhang)
Motivated by the empirical hardness of developing robust classifiers against adversarial perturbations, researchers began asking the question “Does there even exist a robust classifier?”. This is formulated as the intrinsic robustness problem (Mahloujifar et al., 2019), where the goal is to characterize the maximum adversarial robustness possible for a given robust classification problem. Building upon the connection between adversarial robustness and classifier’s error region, it has been shown that if we restrict the search to the set of imperfect classifiers, the intrinsic robustness problem can be reduced to the concentration of measure problem.
Our paper on Improved Estimation of Concentration Under ℓp-Norm Distance Metrics Using Half Spaces (Jack Prescott, Xiao Zhang, and David Evans) will be presented at ICLR 2021.
Abstract: Concentration of measure has been argued to be the fundamental cause of adversarial vulnerability. Mahloujifar et al. (2019) presented an empirical way to measure the concentration of a data distribution using samples, and employed it to find lower bounds on intrinsic robustness for several benchmark datasets.
Xiao Zhang will present Cost-Sensitive Robustness against Adversarial Examples on May 7 (4:30-6:30pm) at ICLR 2019 in New Orleans.
Paper: [PDF] [OpenReview] [ArXiv]
Xiao Zhang and Saeed Mahloujifar will present our work on Empirically Measuring Concentration: Fundamental Limits on Intrinsic Robustness at two workshops May 6 at ICLR 2019 in New Orleans: Debugging Machine Learning Models and Safe Machine Learning:
Specification, Robustness and Assurance.
Xiao Zhang and my paper on Cost-Sensitive Robustness against Adversarial Examples has been accepted to ICLR 2019.
Several recent works have developed methods for training classifiers that are certifiably robust against norm-bounded adversarial perturbations. However, these methods assume that all the adversarial transformations provide equal value for adversaries, which is seldom the case in real-world applications. We advocate for cost-sensitive robustness as the criteria for measuring the classifier’s performance for specific tasks.