{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/second-order-adversarial-attack-and","title":"Certified Adversarial Robustness with Additive Noise","arxiv_id":"1809.03113","date":"2018-09-10","proceeding":"NeurIPS 2019 12","authors":["Bai Li","Changyou Chen","Wenlin Wang","Lawrence Carin"],"abstract":"The existence of adversarial data examples has drawn significant attention in the deep-learning community; such data are seemingly minimally perturbed relative to the original data, but lead to very different outputs from a deep-learning algorithm. 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