{"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/adversarial-examples-are-not-bugs-they-are","title":"Adversarial Examples Are Not Bugs, They Are Features","arxiv_id":"1905.02175","date":"2019-05-06","proceeding":"NeurIPS 2019 12","authors":["Andrew Ilyas","Shibani Santurkar","Dimitris Tsipras","Logan Engstrom","Brandon Tran","Aleksander Madry"],"abstract":"Adversarial examples have attracted significant attention in machine learning, but the reasons for their existence and pervasiveness remain unclear. We demonstrate that adversarial examples can be directly attributed to the presence of non-robust features: features derived from patterns in the data distribution that are highly predictive, yet brittle and incomprehensible to humans. After capturing these features within a theoretical framework, we establish their widespread existence in standard datasets. Finally, we present a simple setting where we can rigorously tie the phenomena we observe in practice to a misalignment between the (human-specified) notion of robustness and the inherent geometry of the data.","url_abs":"https://arxiv.org/abs/1905.02175v4","url_pdf":"https://arxiv.org/pdf/1905.02175v4.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":"adversarial-examples-are-not-bugs-they-are","repo_url":"https://github.com/MadryLab/robustness","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"adversarial-examples-are-not-bugs-they-are","repo_url":"https://github.com/lengstrom/gitlinks","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"adversarial-examples-are-not-bugs-they-are","repo_url":"https://github.com/ndb796/pytorch-adversarial-training-cifar","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"adversarial-examples-are-not-bugs-they-are","repo_url":"https://github.com/xziyue/MNIST_Features","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1905.02175","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.02175"}},"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. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/MadryLab/robustness","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/ndb796/pytorch-adversarial-training-cifar","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/xziyue/MNIST_Features","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/lengstrom/gitlinks","reach":{"status":"unanswered"}}],"summary":{"ran_draft_wrong":2},"by_repo_kind":{"listed":{"samples":2,"ran":2,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":2,"samples":[{"code_sha256_prefix":"33ba52fc17e89516","entry":"mixup_criterion","repo":"ndb796/pytorch-adversarial-training-cifar","repo_kind":"listed","path":"interpolated_adversarial_training.py","file_url":"https://github.com/ndb796/pytorch-adversarial-training-cifar/blob/HEAD/interpolated_adversarial_training.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"33ba52fc17e89516"}},{"code_sha256_prefix":"11b0ec76b88d8553","entry":"mixup_data","repo":"ndb796/pytorch-adversarial-training-cifar","repo_kind":"listed","path":"interpolated_adversarial_training.py","file_url":"https://github.com/ndb796/pytorch-adversarial-training-cifar/blob/HEAD/interpolated_adversarial_training.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"11b0ec76b88d8553"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}