Feb. 8, 2024, 5:10 a.m. | Tsufit Shua Mahmood Sharif

cs.CR updates on arXiv.org arxiv.org

Adversarial examples arose as a challenge for machine learning. To hinder them, most defenses alter how models are trained (e.g., adversarial training) or inference is made (e.g., randomized smoothing). Still, while these approaches markedly improve models' adversarial robustness, models remain highly susceptible to adversarial examples. Identifying that, in certain domains such as traffic-sign recognition, objects are implemented per standards specifying how artifacts (e.g., signs) should be designed, we propose a novel approach for improving adversarial robustness. Specifically, we offer a …

adversarial artifact challenge cs.ai cs.cr cs.cv cs.lg defenses design domains examples machine machine learning recognition robustness sign traffic training

Deputy Chief Information Security Officer

@ United States Holocaust Memorial Museum | Washington, DC

Humbly Confident Security Lead

@ YNAB | Remote

Information Technology Specialist II: Information Security Engineer

@ WBCP, Inc. | Pasadena, CA.

Senior Cloud Security Engineer

@ Cofense | Remote, United States

Cyber Hygiene GCP Cloud Junior Engineer

@ Deutsche Bank | Bucharest

Engineer - Software - Cyber

@ Valeo | BANGALORE - BAN1