Reliable Robustness Evaluation via Automatically Constructed Attack Ensembles

TL;DR

AutoAE automatically constructs attack ensembles for evaluating adversarial robustness, outperforming human-tuned methods and providing a reliable, efficient evaluation protocol across multiple defenses.

Contribution

We introduce AutoAE, a simple algorithm that automatically constructs attack ensembles with provable near-optimality, reducing manual tuning and improving robustness evaluation accuracy.

Findings

AutoAE achieves equal or better robustness evaluation than existing methods.

In 29 out of 45 cases, AutoAE outperforms the best known attack ensembles.

AutoAE is effective across different attack norms and defense models.

Abstract

Attack Ensemble (AE), which combines multiple attacks together, provides a reliable way to evaluate adversarial robustness. In practice, AEs are often constructed and tuned by human experts, which however tends to be sub-optimal and time-consuming. In this work, we present AutoAE, a conceptually simple approach for automatically constructing AEs. In brief, AutoAE repeatedly adds the attack and its iteration steps to the ensemble that maximizes ensemble improvement per additional iteration consumed. We show theoretically that AutoAE yields AEs provably within a constant factor of the optimal for a given defense. We then use AutoAE to construct two AEs for $l_{\infty}$ and $l_2$ attacks, and apply them without any tuning or adaptation to 45 top adversarial defenses on the RobustBench leaderboard. In all except one cases we achieve equal or better (often the latter) robustness evaluation than existing AEs, and notably, in 29 cases we achieve better robustness evaluation than the best known one. Such performance of AutoAE shows itself as a reliable evaluation protocol for adversarial robustness, which further indicates the huge potential of automatic AE construction. Code is available at \url{https://github.com/LeegerPENG/AutoAE}.