Constructing a provably adversarially-robust classifier from a high accuracy one
This addresses the security issue of adversarial attacks in machine learning for applications requiring reliable predictions, though it is incremental as it builds on existing randomized smoothing techniques.
The paper tackles the problem of adversarial vulnerability in high-accuracy classifiers by constructing a new classifier that is provably robust to adversarial perturbations, achieving high accuracy with bounded error under optimal conditions.
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 adversarial $\ell_2$-bounded perturbations. Our algorithm builds upon the framework of \textit{randomized smoothing} that has been recently shown to outperform all previous defenses against $\ell_2$-bounded adversaries. Using techniques like random partitions and doubling dimension, we are able to bound the adversarial error of $g$ in terms of the optimum error. In this paper we focus on our conceptual contribution, but we do present two examples to illustrate our framework. We will argue that, under some assumptions, our bounds are optimal for these cases.