Towards an Empirical Robustness Assessment Through Measuring Adversarial Subspaces

Machine learning (ML) paves the way for innovative applications in various domains. However, adversarial examples pose a significant threat to their robustness, which hinders their usage, for instance, in safety-critical applications. The arms race of attacks and defenses against adversarial examples has received much attention, whilst the analysis on measuring the robustness received little. Robustness scores provide a means to estimate safe regions in the input space for which no adversarial examples exist for a given model. However, these methods often do not scale. On the other hand, empirical investigations have brought the insight that adversarial examples are not isolated examples in the input space, but form contiguous subspaces.