{"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/boosting-adversarial-transferability-through-1","title":"Boosting Adversarial Transferability through Augmentation in Hypothesis Space","arxiv_id":null,"date":"2025-01-01","proceeding":"CVPR 2025 1","authors":["Yu Guo","Weiquan Liu","Qingshan Xu","Shijun Zheng","Shujun Huang","Yu Zang","Siqi Shen","Chenglu Wen","Cheng Wang"],"abstract":"    Adversarial examples can mislead deep neural networks with subtle perturbations, causing them to make incorrect predictions. Notably, adversarial examples crafted for one model can also deceive other models, a phenomenon known as the transferability of adversarial examples. To improve transferability, existing studies have designed increasingly complex mechanisms, but the improvements achieved remain relatively limited and are often difficult to adapt to other modalities, further restricting the scalability of these methods. In this work, we observe a mirroring relationship between model generalization and adversarial example transferability. Motivated by this observation, we propose an augmentation-based attack, called OPS (Operator-Perturbation-based Stochastic optimization), which constructs a stochastic optimization problem by input transformation operators and random perturbations, and solves this problem to generate adversarial examples with better transferability. Extensive experiments on both images and 3D point clouds demonstrate that OPS significantly outperforms existing state-of-the-art methods in terms of both performance and cost, showcasing the universality and superiority of our approach. The code is available at https://github.com/the-full/OPS.    ","url_abs":"http://openaccess.thecvf.com//content/CVPR2025/html/Guo_Boosting_Adversarial_Transferability_through_Augmentation_in_Hypothesis_Space_CVPR_2025_paper.html","url_pdf":"http://openaccess.thecvf.com//content/CVPR2025/papers/Guo_Boosting_Adversarial_Transferability_through_Augmentation_in_Hypothesis_Space_CVPR_2025_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":"boosting-adversarial-transferability-through-1","repo_url":"https://github.com/the-full/ops","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"stochastic-optimization","task_name":"Stochastic Optimization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}