Feb. 8, 2024, 5:10 a.m. | Zhenyu Liu Garrett Gagnon Swagath Venkataramani Liu Liu

cs.CR updates on arXiv.org arxiv.org

Deep Neural Networks (DNNs) have revolutionized a wide range of industries, from healthcare and finance to automotive, by offering unparalleled capabilities in data analysis and decision-making. Despite their transforming impact, DNNs face two critical challenges: the vulnerability to adversarial attacks and the increasing computational costs associated with more complex and larger models. In this paper, we introduce an effective method designed to simultaneously enhance adversarial robustness and execution efficiency. Unlike prior studies that enhance robustness via uniformly injecting noise, we …

adversarial adversarial attacks analysis attacks automotive capabilities challenges computational critical cs.ai cs.cr cs.lg data data analysis decision efficiency finance healthcare impact industries making networks neural networks noise non robustness vulnerability

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