{"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/intriguing-properties-of-neural-networks","title":"Intriguing properties of neural networks","arxiv_id":"1312.6199","date":"2013-12-21","proceeding":null,"authors":["Christian Szegedy","Wojciech Zaremba","Ilya Sutskever","Joan Bruna","Dumitru Erhan","Ian Goodfellow","Rob Fergus"],"abstract":"Deep neural networks are highly expressive models that have recently achieved\nstate of the art performance on speech and visual recognition tasks. While\ntheir expressiveness is the reason they succeed, it also causes them to learn\nuninterpretable solutions that could have counter-intuitive properties. In this\npaper we report two such properties.\n  First, we find that there is no distinction between individual high level\nunits and random linear combinations of high level units, according to various\nmethods of unit analysis. It suggests that it is the space, rather than the\nindividual units, that contains of the semantic information in the high layers\nof neural networks.\n  Second, we find that deep neural networks learn input-output mappings that\nare fairly discontinuous to a significant extend. We can cause the network to\nmisclassify an image by applying a certain imperceptible perturbation, which is\nfound by maximizing the network's prediction error. In addition, the specific\nnature of these perturbations is not a random artifact of learning: the same\nperturbation can cause a different network, that was trained on a different\nsubset of the dataset, to misclassify the same input.","url_abs":"http://arxiv.org/abs/1312.6199v4","url_pdf":"http://arxiv.org/pdf/1312.6199v4.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":"intriguing-properties-of-neural-networks","repo_url":"https://github.com/BendeguzToth/Fun-with-ConvNets","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"intriguing-properties-of-neural-networks","repo_url":"https://github.com/CROWN-Robustness/Crown","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"intriguing-properties-of-neural-networks","repo_url":"https://github.com/IBM/CROWN-Robustness-Certification","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"intriguing-properties-of-neural-networks","repo_url":"https://github.com/Shreyasi2002/Adversarial_Attack_Defense","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"GPL-3.0"}},{"paper_slug":"intriguing-properties-of-neural-networks","repo_url":"https://github.com/andrewilyas/ens-adv-train-attack","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"intriguing-properties-of-neural-networks","repo_url":"https://github.com/anishathalye/neural-hash-collider","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"intriguing-properties-of-neural-networks","repo_url":"https://github.com/huanzhang12/CROWN-Robustness-Certification","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"intriguing-properties-of-neural-networks","repo_url":"https://github.com/iwasakishuto/DeepScreening","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"intriguing-properties-of-neural-networks","repo_url":"https://github.com/kzkadc/adversarial-example-mnist","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"intriguing-properties-of-neural-networks","repo_url":"https://github.com/littleredhat1997/captcha-adversarial-attack","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"intriguing-properties-of-neural-networks","repo_url":"https://github.com/revbucket/mister_ed","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"intriguing-properties-of-neural-networks","repo_url":"https://github.com/ypotdevin/randomized-defenses","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1312.6199","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1312.6199"}},"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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