{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T16:04:04Z","timestamp":1775837044831,"version":"3.50.1"},"reference-count":85,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"2","license":[{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"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":["62125102"],"award-info":[{"award-number":["62125102"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["623B2013"],"award-info":[{"award-number":["623B2013"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key Research and Development Program of China","award":["2022ZD0160401"],"award-info":[{"award-number":["2022ZD0160401"]}]},{"DOI":"10.13039\/501100004826","name":"Beijing Natural Science Foundation","doi-asserted-by":"publisher","award":["JL23005"],"award-info":[{"award-number":["JL23005"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012226","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":[[2025,2]]},"DOI":"10.1109\/tpami.2024.3480519","type":"journal-article","created":{"date-parts":[[2024,10,15]],"date-time":"2024-10-15T17:22:24Z","timestamp":1729012944000},"page":"961-977","source":"Crossref","is-referenced-by-count":63,"title":["Diffusion Models for Imperceptible and Transferable Adversarial Attack"],"prefix":"10.1109","volume":"47","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0031-8417","authenticated-orcid":false,"given":"Jianqi","family":"Chen","sequence":"first","affiliation":[{"name":"Image Processing Center, School of Astronautics, State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6418-3761","authenticated-orcid":false,"given":"Hao","family":"Chen","sequence":"additional","affiliation":[{"name":"Shanghai Artificial Intelligence Laboratory, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0483-1306","authenticated-orcid":false,"given":"Keyan","family":"Chen","sequence":"additional","affiliation":[{"name":"Image Processing Center, School of Astronautics, State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3633-7038","authenticated-orcid":false,"given":"Yilan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Image Processing Center, School of Astronautics, State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1774-552X","authenticated-orcid":false,"given":"Zhengxia","family":"Zou","sequence":"additional","affiliation":[{"name":"Department of Guidance, Navigation and Control, School of Astronautics, Beihang University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4772-3172","authenticated-orcid":false,"given":"Zhenwei","family":"Shi","sequence":"additional","affiliation":[{"name":"Image Processing Center, School of Astronautics, State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-023-05732-2"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA46639.2022.9812137"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.106712"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102301"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2022.3192256"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2022.3180894"},{"key":"ref7","article-title":"Intriguing properties of neural networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Szegedy","year":"2014"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3322785"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3162397"},{"key":"ref10","article-title":"Transferability in machine learning: From phenomena to black-box attacks using adversarial samples","author":"Papernot","year":"2016"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.06083"},{"key":"ref12","article-title":"Explaining and harnessing adversarial examples","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Goodfellow","year":"2015"},{"key":"ref13","article-title":"Decision-based adversarial attacks: Reliable attacks against black-box machine learning models","volume-title":"Int. Conf. Learn. Representations","author":"Brendel","year":"2018"},{"key":"ref14","article-title":"Simple black-box adversarial perturbations for deep networks","author":"Narodytska","year":"2016"},{"key":"ref15","article-title":"Nesterov accelerated gradient and scale invariance for adversarial attacks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Lin","year":"2020"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00957"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00102"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00723"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19772-7_32"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00284"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00444"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00112"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1603.08155"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/470"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01264-9_28"},{"key":"ref26","first-page":"34136","article-title":"Adv-attribute: Inconspicuous and transferable adversarial attack on face recognition","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Jia","year":"2022"},{"key":"ref27","first-page":"16805","article-title":"Diffusion models for adversarial purification","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Nie","year":"2022"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00034"},{"key":"ref29","first-page":"7546","article-title":"Natural color fool: Towards boosting black-box unrestricted attacks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Yuan","year":"2022"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00289"},{"key":"ref32","first-page":"58921","article-title":"Text-to-image diffusion models are zero-shot classifiers","volume-title":"Proc. 2023 Workshop Math. Empirical Understanding Found. Models","author":"Clark","year":"2023"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1145\/3128572.3140448"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01456"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00196"},{"key":"ref36","first-page":"12905","article-title":"Cross-domain transferability of adversarial perturbations","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Naseer","year":"2019"},{"key":"ref37","article-title":"Unrestricted adversarial examples via semantic manipulation","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Bhattad","year":"2020"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58568-6_2"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3261988"},{"key":"ref40","first-page":"36479","article-title":"Photorealistic text-to-image diffusion models with deep language understanding","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Saharia","year":"2022"},{"key":"ref41","article-title":"Hierarchical text-conditional image generation with clip latents","author":"Ramesh","year":"2022"},{"key":"ref42","article-title":"Sdm: Spatial diffusion model for large hole image inpainting","author":"Li","year":"2022"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.02148"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3204461"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1145\/3588432.3591513"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00585"},{"key":"ref47","article-title":"Denoising diffusion implicit models","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Song","year":"2021"},{"key":"ref48","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Ho","year":"2020"},{"key":"ref49","first-page":"2256","article-title":"Deep unsupervised learning using nonequilibrium thermodynamics","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Sohl-Dickstein","year":"2015"},{"key":"ref50","article-title":"Diffedit: Diffusion-based semantic image editing with mask guidance","volume-title":"Proc. 11th Int. Conf. Learn. Representations","author":"Couairon","year":"2023"},{"key":"ref51","first-page":"8780","article-title":"Diffusion models beat gans on image synthesis","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Dhariwal","year":"2021"},{"key":"ref52","article-title":"Prompt-to-prompt image editing with cross attention control","volume-title":"Proc. 11th Int. Conf. Learn. Representations","author":"Hertz","year":"2023"},{"key":"ref53","article-title":"Ensemble adversarial training: Attacks and defenses","volume-title":"Proc. 6th Int. Conf. Learn. Representations","author":"Tram\u00e9r","year":"2018"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2007.383198"},{"key":"ref55","article-title":"Sdedit: Guided image synthesis and editing with stochastic differential equations","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Meng","year":"2021"},{"key":"ref56","article-title":"The caltech-UCSD birds-200-2011 dataset","author":"Wah","year":"2011"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW.2013.77"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref60","article-title":"Very deep convolutional networks for large-scale image recognition","volume-title":"Proc. 11th Int. Conf. Learn. Representations","author":"Simonyan","year":"2015"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref63","article-title":"An image is worth 16x16 words: Transformers for image recognition at scale","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Dosovitskiy","year":"2021"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref65","first-page":"10347","article-title":"Training data-efficient image transformers & distillation through attention","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Touvron","year":"2021"},{"key":"ref66","first-page":"24261","article-title":"MLP-mixer: An all-MLP architecture for vision","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Tolstikhin","year":"2021"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2019.2940533"},{"key":"ref68","article-title":"Mitigating adversarial effects through randomization","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Xie","year":"2018"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00191"},{"key":"ref70","article-title":"Defense against adversarial attacks-3rd place","author":"Thomas","year":"2017"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-94042-7_11"},{"key":"ref72","article-title":"Decoupled weight decay regularization","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Loshchilov","year":"2019"},{"key":"ref73","first-page":"6629","article-title":"GANs trained by a two time-scale update rule converge to a local nash equilibrium","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Heusel","year":"2017"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00068"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58604-1_19"},{"key":"ref76","first-page":"10408","article-title":"Functional adversarial attacks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Laidlaw","year":"2019"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00465"},{"key":"ref78","article-title":"Beyond imagenet attack: Towards crafting adversarial examples for black-box domains","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Zhang","year":"2022"},{"key":"ref79","first-page":"14963","article-title":"Transferable sparse adversarial attack","volume-title":"Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit.","author":"He","year":"2022"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref81","article-title":"Adversarial machine learning at scale","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Kurakin","year":"2017"},{"key":"ref82","first-page":"8748","article-title":"Learning transferable visual models from natural language supervision","volume-title":"Proc. Int. conf. Mach. Learn.","author":"Radford","year":"2021"},{"key":"ref83","article-title":"Diffusion-based adversarial sample generation for improved stealthiness and controllability","volume":"36","author":"Xue","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref84","article-title":"Semantic adversarial attacks via diffusion models","volume-title":"Proc. 34th British Mach. Vis. Conf.","author":"Wang","year":"2023"},{"key":"ref85","article-title":"Content-based unrestricted adversarial attack","volume":"36","author":"Chen","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/34\/10835210\/10716799.pdf?arnumber=10716799","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,15]],"date-time":"2025-01-15T19:53:55Z","timestamp":1736970835000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10716799\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2]]},"references-count":85,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2024.3480519","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":[[2025,2]]}}}