May 16, 2024, 4:41 a.m. | Changming Xu, Gagandeep Singh

cs.LG updates on

arXiv:2405.09176v1 Announce Type: new
Abstract: Existing work in trustworthy machine learning primarily focuses on single-input adversarial perturbations. In many real-world attack scenarios, input-agnostic adversarial attacks, e.g. universal adversarial perturbations (UAPs), are much more feasible. Current certified training methods train models robust to single-input perturbations but achieve suboptimal clean and UAP accuracy, thereby limiting their applicability in practical applications. We propose a novel method, CITRUS, for certified training of networks robust against UAP attackers. We show in an extensive evaluation across …

abstract accuracy adversarial adversarial attacks arxiv attacks cs.lg current machine machine learning robust train training trustworthy type uaps universal work world

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