{"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/feature-space-perturbations-yield-more","title":"Feature Space Perturbations Yield More Transferable Adversarial Examples","arxiv_id":null,"date":"2019-06-01","proceeding":"CVPR 2019 6","authors":["Nathan Inkawhich"," Wei Wen"," Hai (Helen) Li"," Yiran Chen"],"abstract":"Many recent works have shown that deep learning models are vulnerable to quasi-imperceptible input perturbations, yet practitioners cannot fully explain this behavior. This work describes a transfer-based blackbox targeted adversarial attack of deep feature space representations that also provides insights into cross-model class representations of deep CNNs. The attack is explicitly designed for transferability and drives feature space representation of a source image at layer L towards the representation of a target image at L. The attack yields highly transferable targeted examples, which outperform competition winning methods by over 30% in targeted attack metrics. We also show the choice of L to generate examples from is important, transferability characteristics are blackbox model agnostic, and indicate that well trained deep models have similar highly-abstract representations.\r","url_abs":"http://openaccess.thecvf.com/content_CVPR_2019/html/Inkawhich_Feature_Space_Perturbations_Yield_More_Transferable_Adversarial_Examples_CVPR_2019_paper.html","url_pdf":"http://openaccess.thecvf.com/content_CVPR_2019/papers/Inkawhich_Feature_Space_Perturbations_Yield_More_Transferable_Adversarial_Examples_CVPR_2019_paper.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":"feature-space-perturbations-yield-more","repo_url":"https://github.com/Shall-Ven/Activation-Attack","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"adversarial-attack","task_name":"Adversarial Attack"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}