{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T04:50:27Z","timestamp":1779166227641,"version":"3.51.4"},"reference-count":71,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100002597","name":"Kyung Hee University","doi-asserted-by":"publisher","award":["KHU-20241101"],"award-info":[{"award-number":["KHU-20241101"]}],"id":[{"id":"10.13039\/501100002597","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Multimedia"],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/tmm.2024.3521769","type":"journal-article","created":{"date-parts":[[2024,12,24]],"date-time":"2024-12-24T19:21:58Z","timestamp":1735068118000},"page":"1901-1913","source":"Crossref","is-referenced-by-count":6,"title":["Black-Box Targeted Adversarial Attack on Segment Anything (SAM)"],"prefix":"10.1109","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0567-7868","authenticated-orcid":false,"given":"Sheng","family":"Zheng","sequence":"first","affiliation":[{"name":"School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6007-6099","authenticated-orcid":false,"given":"Chaoning","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computing, Kyung Hee University, Seoul, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6448-4839","authenticated-orcid":false,"given":"Xinhong","family":"Hao","sequence":"additional","affiliation":[{"name":"School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","article-title":"On the opportunities and risks of foundation models","author":"Bommasani","year":"2021"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/n19-1423"},{"key":"ref3","first-page":"1877","article-title":"Language models are few-shot learners","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Brown","year":"2020"},{"key":"ref4","article-title":"Improving language understanding by generative pre-training","author":"Radford","year":"2018"},{"key":"ref5","article-title":"Language models are unsupervised multitask learners","volume":"1","author":"Radford","year":"2019","journal-title":"OpenAI Blog"},{"key":"ref6","article-title":"One small step for generative AI, one giant leap for agi: A complete survey on ChatGPT in AIGC era","author":"Zhang","year":"2023"},{"key":"ref7","article-title":"Text-to-image diffusion models in generative AI: A survey","author":"Zhang","year":"2023"},{"key":"ref8","article-title":"A survey on audio diffusion models: Text to speech synthesis and enhancement in generative AI","author":"Zhang","year":"2023"},{"key":"ref9","article-title":"Generative AI meets 3D: A. survey on text-to-3D in AIGC era","author":"Li","year":"2023"},{"key":"ref10","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":"ref11","first-page":"4904","article-title":"Scaling up visual and vision-language representation learning with noisy text supervision","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Jia","year":"2021"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01058"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2023.3327924"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-47401-9_13"},{"key":"ref16","article-title":"Grounded SAM: Assembling open-world models for diverse visual tasks","author":"Ren","year":"2024"},{"key":"ref17","article-title":"Track anything: Segment anything meets videos","author":"Yang","year":"2023"},{"key":"ref18","article-title":"Intriguing properties of neural networks","volume-title":"Proc. 2nd Int. Conf. Learn. Representations","author":"Szegedy","year":"2014"},{"key":"ref19","article-title":"Explaining and harnessing adversarial examples","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Goodfellow","year":"2015"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.06083"},{"key":"ref21","article-title":"Attack-SAM: Towards evaluating adversarial robustness of segment anything model","author":"Zhang","year":"2023"},{"key":"ref22","first-page":"125","article-title":"Adversarial examples are not bugs, they are features","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Ilyas","year":"2019"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-40994-3_25"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2022.3173533"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01453"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01466"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2023.3327017"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2021.3050057"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2021.3108009"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58601-0_24"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58574-7_41"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2021.3079723"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00957"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00444"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00284"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2022.3211736"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2023.3315550"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2021.3097506"},{"key":"ref39","article-title":"Nesterov accelerated gradient and scale invariance for adversarial attacks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Lin","year":"2020"},{"key":"ref40","first-page":"272","article-title":"Boosting adversarial transferability through enhanced momentum","volume-title":"Proc. Brit. Mach. Vis. Conf.","author":"Wang","year":"2021"},{"key":"ref41","article-title":"Improving adversarial transferability via intermediate-level perturbation decay","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"36","author":"Li","year":"2024"},{"key":"ref42","first-page":"6115","article-title":"On success and simplicity: A second look at transferable targeted attacks","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Zhao","year":"2021"},{"key":"ref43","article-title":"Fast segment anything","author":"Zhao","year":"2023"},{"key":"ref44","article-title":"Faster segment anything: Towards lightweight SAM for mobile applications","author":"Zhang","year":"2023"},{"key":"ref45","article-title":"Attack-sam: Towards evaluating adversarial robustness of segment anything model","author":"Zhang","year":"2023"},{"key":"ref46","article-title":"Robustness of sam: Segment anything under corruptions and beyond","author":"Qiao","year":"2023"},{"key":"ref47","article-title":"RepViT-SAM: Towards real-time segmenting anything","author":"Wang","year":"2023"},{"key":"ref48","article-title":"EdgeSAM: Prompt-in-the-loop distillation for on-device deployment of SAM","author":"Zhou","year":"2023"},{"key":"ref49","article-title":"Segment anything model (SAM) meets glass: Mirror and transparent objects cannot be easily detected","author":"Han","year":"2023"},{"key":"ref50","first-page":"13842","article-title":"Adversarial robustness through local linearization","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Qin","year":"2019"},{"key":"ref51","first-page":"29935","article-title":"Data augmentation can improve robustness","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Rebuffi","year":"2021"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2003.819861"},{"key":"ref53","article-title":"Data from model: Extracting data from non-robust and robust models","volume-title":"Proc. CVPR Workshop Adversarial Mach. Learn. Comput. Vis.","author":"Benz","year":"2020"},{"key":"ref54","article-title":"Insects semantic segmentation dataset","year":"2024"},{"key":"ref55","article-title":"A large annotated medical image dataset for the development and evaluation of segmentation algorithms","author":"Simpson","year":"2019"},{"key":"ref56","first-page":"7472","article-title":"Theoretically principled trade-off between robustness and accuracy","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zhang","year":"2019"},{"key":"ref57","article-title":"Universal adversarial perturbations are not bugs, they are features","volume-title":"Proc. CVPR Workshop Adversarial Mach. Learn. Comput. Vis.","author":"Benz","year":"2020"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2018.23198"},{"key":"ref59","article-title":"A study of the effect of JPG compression on adversarial images","author":"Dziugaite","year":"2016"},{"key":"ref60","article-title":"JPEG-resistant adversarial images","volume-title":"Proc. NIPS 2017 Workshop Mach. Learn. Comput. Secur.","author":"Shin","year":"2017"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-94042-7_11"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"ref63","article-title":"Can SAM segment anything? When SAM meets camouflaged object detection","author":"Tang","year":"2023"},{"key":"ref64","article-title":"Segment anything meets semantic communication","author":"Tariq","year":"2023"},{"key":"ref65","article-title":"Scalable mask annotation for video text spotting","author":"He","year":"2023"},{"key":"ref66","article-title":"Weakly-supervised concealed object segmentation with SAM-based pseudo labeling and multi-scale feature grouping","author":"He","year":"2023"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-73195-2_27"},{"key":"ref68","article-title":"Edit everything: A text-guided generative system for images editing","author":"Xie","year":"2023"},{"key":"ref69","article-title":"UVOSAM: A mask-free paradigm for unsupervised video object segmentation via segment anything model","author":"Zhang","year":"2023"},{"key":"ref70","first-page":"25971","article-title":"Segment anything in 3D with NeRFs","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"36","author":"Cen","year":"2023"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1007\/s11432-023-3943-6"}],"container-title":["IEEE Transactions on Multimedia"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6046\/10844992\/10814684.pdf?arnumber=10814684","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,7]],"date-time":"2025-04-07T21:56:12Z","timestamp":1744062972000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10814684\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":71,"URL":"https:\/\/doi.org\/10.1109\/tmm.2024.3521769","relation":{},"ISSN":["1520-9210","1941-0077"],"issn-type":[{"value":"1520-9210","type":"print"},{"value":"1941-0077","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}