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We\npropose a systematic algorithm for computing universal perturbations, and show\nthat state-of-the-art deep neural networks are highly vulnerable to such\nperturbations, albeit being quasi-imperceptible to the human eye. We further\nempirically analyze these universal perturbations and show, in particular, that\nthey generalize very well across neural networks. The surprising existence of\nuniversal perturbations reveals important geometric correlations among the\nhigh-dimensional decision boundary of classifiers. It further outlines\npotential security breaches with the existence of single directions in the\ninput space that adversaries can possibly exploit to break a classifier on most\nnatural images.","url_abs":"http://arxiv.org/abs/1610.08401v3","url_pdf":"http://arxiv.org/pdf/1610.08401v3.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":"universal-adversarial-perturbations","repo_url":"https://github.com/LTS4/universal","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"universal-adversarial-perturbations","repo_url":"https://github.com/BXuan694/Universal-Adversarial-Perturbation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"universal-adversarial-perturbations","repo_url":"https://github.com/BXuan694/universalAdversarialPerturbation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"universal-adversarial-perturbations","repo_url":"https://github.com/NetoPedro/Universal-Adversarial-Perturbations-Pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"universal-adversarial-perturbations","repo_url":"https://github.com/bingcheng45/hnr-extension","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"universal-adversarial-perturbations","repo_url":"https://github.com/ferjad/Universal_Adverserial_Perturbation_pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"universal-adversarial-perturbations","repo_url":"https://github.com/riiswa/universal","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"universal-adversarial-perturbations","repo_url":"https://github.com/ssg-research/flare","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1610.08401","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1610.08401"}},"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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