{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T17:45:37Z","timestamp":1777657537995,"version":"3.51.4"},"reference-count":55,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"5","license":[{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52005505"],"award-info":[{"award-number":["52005505"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Pattern Anal. Mach. Intell."],"published-print":{"date-parts":[[2025,5]]},"DOI":"10.1109\/tpami.2025.3535933","type":"journal-article","created":{"date-parts":[[2025,1,29]],"date-time":"2025-01-29T18:52:31Z","timestamp":1738176751000},"page":"3975-3991","source":"Crossref","is-referenced-by-count":2,"title":["A3: A Platform of Searching for Robust Neural Architectures and Efficient Adversarial Attacks"],"prefix":"10.1109","volume":"47","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4482-1279","authenticated-orcid":false,"given":"Jialiang","family":"Sun","sequence":"first","affiliation":[{"name":"Defense Innovation Institute, Chinese Academy of Military Science, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5224-9834","authenticated-orcid":false,"given":"Wen","family":"Yao","sequence":"additional","affiliation":[{"name":"Defense Innovation Institute, Chinese Academy of Military Science, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1637-2928","authenticated-orcid":false,"given":"Tingsong","family":"Jiang","sequence":"additional","affiliation":[{"name":"Defense Innovation Institute, Chinese Academy of Military Science, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2391-7319","authenticated-orcid":false,"given":"Chao","family":"Li","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Xidian University, Xian, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9255-3371","authenticated-orcid":false,"given":"Xiaoqian","family":"Chen","sequence":"additional","affiliation":[{"name":"Defense Innovation Institute, Chinese Academy of Military Science, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3127851"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1080\/01431160600746456"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2018.2876865"},{"key":"ref4","article-title":"Explaining and harnessing adversarial examples","volume-title":"Proc. 3rd Int. Conf. Learn. Representations","author":"Goodfellow"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.06083"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.49"},{"key":"ref7","article-title":"Technical report on the cleverhans v2. 1.0 adversarial examples library","author":"Papernot","year":"2016"},{"key":"ref8","article-title":"Foolbox: A python toolbox to benchmark the robustness of machine learning models","author":"Rauber","year":"2017"},{"key":"ref9","article-title":"Adversarial robustness toolbox v1. 0.0","author":"Nicolae","year":"2018"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00040"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.acl-demo.43"},{"key":"ref12","article-title":"Deeprobust: A pytorch library for adversarial attacks and defenses","author":"Li","year":"2020"},{"key":"ref13","article-title":"DARTS: Differentiable architecture search","author":"Liu","year":"2018"},{"key":"ref14","article-title":"PC-darts: Partial channel connections for memory-efficient architecture search","author":"Xu","year":"2019"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00613"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01210"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.04.111"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i10.17075"},{"key":"ref19","article-title":"Automated discovery of adaptive attacks on adversarial defenses","author":"Yao","year":"2021"},{"key":"ref20","article-title":"Explaining and harnessing adversarial examples","author":"Goodfellow","year":"2014"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.49"},{"key":"ref22","article-title":"An alternative surrogate loss for PGD-based adversarial testing","author":"Gowal","year":"2019"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00957"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2016.41"},{"key":"ref25","article-title":"Dimensionality reduction as a defense against evasion attacks on machine learning classifiers","author":"Arjun","year":"2017"},{"key":"ref26","article-title":"Countering adversarial images using input transformations","author":"Guo","year":"2017"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00071"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.6143"},{"key":"ref29","article-title":"Automated decision-based adversarial attacks","author":"Fu","year":"2021"},{"key":"ref30","first-page":"2206","article-title":"Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks","volume-title":"Proc. 37th Int. Conf. Mach. Learn.","author":"Croce"},{"key":"ref31","article-title":"Evolving robust neural architectures to defend from adversarial attacks","author":"Vargas","year":"2019"},{"key":"ref32","article-title":"Benchmarking neural network robustness to common corruptions and perturbations","author":"Hendrycks","year":"2019"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1145\/3475724.3483607"},{"key":"ref34","first-page":"1554","article-title":"Stabilizing differentiable architecture search via perturbation-based regularization","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Chen"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58555-6_28"},{"key":"ref36","first-page":"367","article-title":"Random search and reproducibility for neural architecture search","volume-title":"Proc. Uncertainty Artif. Intell.","author":"Li"},{"key":"ref37","article-title":"Differential evolution for neural architecture search","author":"Awad","year":"2020"},{"key":"ref38","article-title":"Neural architecture optimization","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Luo"},{"key":"ref39","first-page":"4095","article-title":"Efficient neural architecture search via parameters sharing","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Pham"},{"key":"ref40","first-page":"1808","article-title":"Bridging the gap between sample-based and one-shot neural architecture search with bonas","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Shi"},{"key":"ref41","first-page":"2958","article-title":"Adversarial weight perturbation helps robust generalization","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Wu"},{"key":"ref42","article-title":"Improving adversarial robustness requires revisiting misclassified examples","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Wang"},{"key":"ref43","first-page":"7472","article-title":"Theoretically principled trade-off between robustness and accuracy","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zhang"},{"key":"ref44","first-page":"1831","article-title":"Defense against adversarial attacks using feature scattering-based adversarial training","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Zhang"},{"key":"ref45","article-title":"Adversarial interpolation training: A simple approach for improving model robustness","author":"Zhang","year":"2019"},{"key":"ref46","article-title":"Manifold regularization for adversarial robustness","author":"Jin","year":"2020"},{"key":"ref47","article-title":"Fixing data augmentation to improve adversarial robustness","author":"Rebuffi","year":"2021"},{"key":"ref48","article-title":"Towards achieving adversarial robustness beyond perceptual limits","author":"Addepalli","year":"2021"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01543"},{"key":"ref50","first-page":"8093","article-title":"Overfitting in adversarially robust deep learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Rice"},{"key":"ref51","article-title":"Luring of adversarial perturbations","volume-title":"Proc. Actes De La Conf. CAID 2020","author":"Bernhard"},{"key":"ref52","article-title":"Fast is better than free: Revisiting adversarial training","author":"Wong","year":"2020"},{"key":"ref53","article-title":"Label smoothing and logit squeezing: A replacement for adversarial training?","author":"Shafahi","year":"2019"},{"key":"ref54","article-title":"Ensemble adversarial training: Attacks and defenses","author":"Tram\u00e8r","year":"2017"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00929"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/34\/10958761\/10857641.pdf?arnumber=10857641","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,14]],"date-time":"2025-04-14T18:19:15Z","timestamp":1744654755000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10857641\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5]]},"references-count":55,"journal-issue":{"issue":"5"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2025.3535933","relation":{},"ISSN":["0162-8828","2160-9292","1939-3539"],"issn-type":[{"value":"0162-8828","type":"print"},{"value":"2160-9292","type":"electronic"},{"value":"1939-3539","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5]]}}}