{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,3]],"date-time":"2026-10-03T20:34:46Z","timestamp":1791059686262,"version":"4.1.0"},"reference-count":61,"publisher":"IEEE","license":[{"start":{"date-parts":[[2025,10,19]],"date-time":"2025-10-19T00:00:00Z","timestamp":1760832000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,10,19]],"date-time":"2025-10-19T00:00:00Z","timestamp":1760832000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100005089","name":"Beijing Natural Science Foundation","doi-asserted-by":"publisher","award":["L252145"],"award-info":[{"award-number":["L252145"]}],"id":[{"id":"10.13039\/501100005089","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62425606,32341009,U21B2045"],"award-info":[{"award-number":["62425606,32341009,U21B2045"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,19]]},"DOI":"10.1109\/iccv51701.2025.01793","type":"proceedings-article","created":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T19:45:49Z","timestamp":1777491949000},"page":"19290-19300","source":"Crossref","is-referenced-by-count":3,"title":["Towards Robust Defense Against Customization via Protective Perturbation Resistant to Diffusion-based Purification"],"prefix":"10.1109","author":[{"given":"Wenkui","family":"Yang","sequence":"first","affiliation":[{"name":"MAIS & NLPR, Institute of Automation, Chinese Academy of Sciences"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Cao","sequence":"additional","affiliation":[{"name":"MAIS & NLPR, Institute of Automation, Chinese Academy of Sciences"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junxian","family":"Duan","sequence":"additional","affiliation":[{"name":"MAIS & NLPR, Institute of Automation, Chinese Academy of Sciences"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ran","family":"He","sequence":"additional","affiliation":[{"name":"MAIS & NLPR, Institute of Automation, Chinese Academy of Sciences"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52734.2025.02682"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.02062"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/FG.2018.00020"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00916"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00482"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00525"},{"key":"ref7","first-page":"8780","article-title":"Diffusion models beat gans on image synthesis","volume-title":"Adv. Neural Inform. Process. Syst.","author":"Dhariwal","year":"2021"},{"key":"ref8","article-title":"An image is worth one word: Personalizing text-to-image generation using textual inversion","volume-title":"Int. Conf. Learn. Represent.","author":"Gal","year":"2023"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.5555\/2969033.2969125"},{"key":"ref10","article-title":"Explaining and harnessing adversarial examples","volume-title":"Int. Conf. Learn. Represent.","author":"Goodfellow","year":"2015"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2019.2916751"},{"key":"ref12","article-title":"Gans trained by a two time-scale update rule converge to a local nash equilibrium","author":"Heusel","year":"2017","journal-title":"Adv. Neural Inform. Process. Syst."},{"key":"ref13","article-title":"Classifier-free diffusion guidance","author":"Ho","year":"2021","journal-title":"Adv. Neural Inform. Process. Syst. Worksh."},{"key":"ref14","first-page":"6840","article-title":"Denoising diffusion probabilistic models","author":"Ho","year":"2020","journal-title":"Adv. Neural Inform. Process. Syst."},{"key":"ref15","first-page":"2790","article-title":"Parameter-efficient transfer learning for nlp","volume-title":"Int. Conf. Mach. Learn.","author":"Houlsby","year":"2019"},{"key":"ref16","article-title":"Lora: Low-rank adaptation of large language models","volume-title":"Int. Conf. Learn. Represent.","author":"Hu","year":"2022"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i1.19982"},{"key":"ref18","article-title":"Progressive growing of gans for improved quality, stability, and variation","author":"Karras","year":"2017","journal-title":"arXiv preprint"},{"key":"ref19","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inform. Process. Syst."},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00192"},{"key":"ref21","article-title":"Adversarial machine learning at scale","volume-title":"Int. Conf. Learn. Represent.","author":"Kurakin","year":"2017"},{"key":"ref22","article-title":"Adversarial examples in the physical world","author":"Kurakin","year":"2017","journal-title":"arXiv preprint"},{"key":"ref23","article-title":"Mist: Towards improved adversarial examples for diffusion models","author":"Liang","year":"2023","journal-title":"arXiv preprint"},{"key":"ref24","article-title":"Adversarial example does good: Preventing painting imitation from diffusion models via adversarial examples","volume-title":"Int. Conf. Mach. Learn.","author":"Liang","year":"2023"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.02286"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.06083"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2012.2214050"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i5.28226"},{"key":"ref29","article-title":"Glide: Towards photorealistic image generation and editing with text-guided diffusion models","author":"Nichol","year":"2022","journal-title":"Adv. Neural Inform. Process. Syst."},{"key":"ref30","article-title":"Diffusion models for adversarial purification","volume-title":"Int. Conf. Mach. Learn.","author":"Nie","year":"2022"},{"key":"ref31","first-page":"8821","article-title":"Zero-shot text-to-image generation","volume-title":"Int. Conf. Mach. Learn.","author":"Ramesh","year":"2021"},{"issue":"2","key":"ref32","article-title":"Hierarchical text-conditional image generation with clip latents","volume":"1","author":"Ramesh","year":"2022","journal-title":"arXiv preprint"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-66823-5_14"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.02155"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.52202\/068431-2643"},{"key":"ref38","article-title":"Raising the cost of malicious ai-powered image editing","volume-title":"Int. Conf. Mach. Learn.","author":"Salman","year":"2023"},{"key":"ref39","first-page":"2187","article-title":"Glaze: Protecting artists from style mimicry by \\{Text-to-Image\\} models","volume-title":"USENIX Security Symp.","author":"Shan","year":"2023"},{"key":"ref40","article-title":"Very deep convolutional networks for large-scale image recognition","volume-title":"Int. Conf. Learn. Represent.","author":"Simonyan","year":"2015"},{"key":"ref41","first-page":"2256","article-title":"Deep unsupervised learning using nonequilibrium thermodynamics","volume-title":"Int. Conf. Mach. Learn.","author":"Sohl-Dickstein","year":"2015"},{"key":"ref42","article-title":"Denoising diffusion implicit models","volume-title":"Int. Conf. Learn. Represent.","author":"Song","year":"2021"},{"key":"ref43","article-title":"Generative modeling by estimating gradients of the data distribution","author":"Song","year":"2019","journal-title":"Adv. Neural Inform. Process. Syst."},{"key":"ref44","article-title":"Score-based generative modeling through stochastic differential equations","volume-title":"Int. Conf. Learn. Represent.","author":"Song","year":"2021"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr52734.2025.00287"},{"key":"ref46","volume-title":"Stable diffusion","year":"2022"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.308"},{"issue":"11","key":"ref48","article-title":"Visualizing data using t-sne","volume":"9","author":"Van der Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00202"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.01145"},{"key":"ref51","article-title":"Guided diffusion model for adversarial purification","author":"Wang","year":"2022","journal-title":"arXiv preprint"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2020.2967289"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00917"},{"key":"ref54","article-title":"Densepure: Understanding diffusion models for adversarial robustness","volume-title":"Int. Conf. Learn. Represent.","author":"Xiao","year":"2023"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.02316"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/WACVW50321.2020.9096939"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00355"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00068"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.02303"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/iccv.2017.244"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.02305"}],"event":{"name":"2025 IEEE\/CVF International Conference on Computer Vision (ICCV)","location":"Honolulu, HI, USA","start":{"date-parts":[[2025,10,19]]},"end":{"date-parts":[[2025,10,25]]}},"container-title":["2025 IEEE\/CVF International Conference on Computer Vision (ICCV)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11443115\/11443287\/11444215.pdf?arnumber=11444215","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T05:29:17Z","timestamp":1777613357000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11444215\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,19]]},"references-count":61,"URL":"https:\/\/doi.org\/10.1109\/iccv51701.2025.01793","relation":{},"subject":[],"published":{"date-parts":[[2025,10,19]]}}}