{"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/fast-minimum-norm-adversarial-attacks-through","title":"Fast Minimum-norm Adversarial Attacks through Adaptive Norm Constraints","arxiv_id":"2102.12827","date":"2021-02-25","proceeding":"NeurIPS 2021 12","authors":["Maura Pintor","Fabio Roli","Wieland Brendel","Battista Biggio"],"abstract":"Evaluating adversarial robustness amounts to finding the minimum perturbation needed to have an input sample misclassified. The inherent complexity of the underlying optimization requires current gradient-based attacks to be carefully tuned, initialized, and possibly executed for many computationally-demanding iterations, even if specialized to a given perturbation model. In this work, we overcome these limitations by proposing a fast minimum-norm (FMN) attack that works with different $\\ell_p$-norm perturbation models ($p=0, 1, 2, \\infty$), is robust to hyperparameter choices, does not require adversarial starting points, and converges within few lightweight steps. It works by iteratively finding the sample misclassified with maximum confidence within an $\\ell_p$-norm constraint of size $\\epsilon$, while adapting $\\epsilon$ to minimize the distance of the current sample to the decision boundary. Extensive experiments show that FMN significantly outperforms existing attacks in terms of convergence speed and computation time, while reporting comparable or even smaller perturbation sizes.","url_abs":"https://arxiv.org/abs/2102.12827v3","url_pdf":"https://arxiv.org/pdf/2102.12827v3.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":"fast-minimum-norm-adversarial-attacks-through","repo_url":"https://github.com/pralab/Fast-Minimum-Norm-FMN-Attack","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"fast-minimum-norm-adversarial-attacks-through","repo_url":"https://github.com/jeromerony/adversarial-library","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"fast-minimum-norm-adversarial-attacks-through","repo_url":"https://github.com/bethgelab/foolbox","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"adversarial-attack","task_name":"Adversarial Attack"},{"task_slug":"adversarial-robustness","task_name":"Adversarial Robustness"}],"methods":[{"method_slug":"fast-minimum-norm-attack","method_name":"Fast Minimum-Norm Attack"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2102.12827","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2102.12827"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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