{"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/defending-against-whitebox-adversarial","title":"Defending against Whitebox Adversarial Attacks via Randomized Discretization","arxiv_id":"1903.10586","date":"2019-03-25","proceeding":null,"authors":["Yuchen Zhang","Percy Liang"],"abstract":"Adversarial perturbations dramatically decrease the accuracy of\nstate-of-the-art image classifiers. In this paper, we propose and analyze a\nsimple and computationally efficient defense strategy: inject random Gaussian\nnoise, discretize each pixel, and then feed the result into any pre-trained\nclassifier. Theoretically, we show that our randomized discretization strategy\nreduces the KL divergence between original and adversarial inputs, leading to a\nlower bound on the classification accuracy of any classifier against any\n(potentially whitebox) $\\ell_\\infty$-bounded adversarial attack. Empirically,\nwe evaluate our defense on adversarial examples generated by a strong iterative\nPGD attack. On ImageNet, our defense is more robust than adversarially-trained\nnetworks and the winning defenses of the NIPS 2017 Adversarial Attacks &\nDefenses competition.","url_abs":"http://arxiv.org/abs/1903.10586v1","url_pdf":"http://arxiv.org/pdf/1903.10586v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"defending-against-whitebox-adversarial","repo_url":"https://worksheets.codalab.org/worksheets/0x822ba2f9005f49f08755a84443c76456","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"adversarial-attack","task_name":"Adversarial Attack"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1903.10586","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}