# MALT Powers Up Adversarial Attacks Source: [MALT Powers Up Adversarial Attacks]() Collection index: [llms.txt]() Data updated: 2026-01-04T04:53:09.815Z Authors: Odelia Melamed, Gilad Yehudai, Adi Shamir Published: 2024-07-02T13:02:12.000Z ## Abstract Current adversarial attacks for multi-class classifiers choose the target class for a given input naively, based on the classifier's confidence levels for various target classes. We present a novel adversarial targeting method, \\textit{MALT - Mesoscopic Almost Linearity Targeting}, based on medium-scale almost linearity assumptions. Our attack wins over the current state of the art AutoAttack on the standard benchmark datasets CIFAR-100 and ImageNet and for a variety of robust models. In particular, our attack is \\emph{five times faster} than AutoAttack, while successfully matching all of AutoAttack's successes and attacking additional samples that were previously out of reach. We then prove formally and demonstrate empirically that our targeting method, although inspired by linear predictors, also applies to standard non-linear models. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Image Classification on ImageNet (test) (MALT/SOTA Robustness and Speed)](): Image Classification; ImageNet (test); Accuracy: 79.18. - [Image Classification on CIFAR100 (test) (ACC, MALT, DIFF)](): Image Classification; CIFAR100 (test); Acc: 75.22. ## Original sources and implementation [Original paper]() [PDF]() [Implementation]()