{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T15:30:39Z","timestamp":1783611039140,"version":"3.55.0"},"reference-count":55,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":["62272347"],"award-info":[{"award-number":["62272347"]}],"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":["U19B2004"],"award-info":[{"award-number":["U19B2004"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research Development Program of China","doi-asserted-by":"publisher","award":["2022YFB2901202"],"award-info":[{"award-number":["2022YFB2901202"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans.Inform.Forensic Secur."],"published-print":{"date-parts":[[2023]]},"DOI":"10.1109\/tifs.2023.3244107","type":"journal-article","created":{"date-parts":[[2023,2,10]],"date-time":"2023-02-10T14:27:48Z","timestamp":1676039268000},"page":"1473-1486","source":"Crossref","is-referenced-by-count":14,"title":["Categorical Inference Poisoning: Verifiable Defense Against Black-Box DNN Model Stealing Without Constraining Surrogate Data and Query Times"],"prefix":"10.1109","volume":"18","author":[{"given":"Haitian","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Electronic Information, Wuhan University, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5184-4359","authenticated-orcid":false,"given":"Guang","family":"Hua","sequence":"additional","affiliation":[{"name":"Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinya","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electronic Information, Wuhan University, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8533-1612","authenticated-orcid":false,"given":"Hao","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Electronic Information, Wuhan University, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3263-8768","authenticated-orcid":false,"given":"Wen","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Electronic Information, Wuhan University, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-15398-4_13"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1145\/3078971.3078974"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.07.051"},{"key":"ref5","first-page":"4716","article-title":"Rethinking deep neural network ownership verification: Embedding passports to defeat ambiguity attacks","volume-title":"Proc. NeurIPS","author":"Fan"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-55393-7_33"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3450000"},{"key":"ref8","first-page":"1615","article-title":"Turning your weakness into a strength: Watermarking deep neural networks by backdooring","volume-title":"Proc. 27th USENIX Secur. Symp. (USENIX Secur.)","author":"Adi"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1186\/s13640-020-00527-1"},{"key":"ref10","first-page":"1937","article-title":"Entangled watermarks as a defense against model extraction","volume-title":"Proc. 30th USENIX Secur. Symp. (USENIX Secur.)","author":"Jia"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/3474085.3475591"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.5555\/3241094.3241142"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1145\/3052973.3053009"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/3274694.3274740"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2018.8489592"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00509"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5432"},{"key":"ref18","first-page":"20120","article-title":"Black-box ripper: Copying black-box models using generative evolutionary algorithms","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Barbalau"},{"key":"ref19","first-page":"1345","article-title":"High accuracy and high fidelity extraction of neural networks","volume-title":"Proc. 29th USENIX Secur. Symp. (USENIX Secur.)","author":"Jagielski"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00473"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00474"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01360"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01029"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1145\/3460231.3474275"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/SPW.2019.00020"},{"key":"ref26","first-page":"1","article-title":"Prediction poisoning: Towards defenses against DNN model stealing attacks","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Orekondy"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2021.3069258"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/EuroSP.2019.00044"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00085"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1145\/3433210.3437526"},{"key":"ref31","first-page":"1","article-title":"Deep neural network fingerprinting by conferrable adversarial examples","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Lukas"},{"key":"ref32","article-title":"Dataset inference: Ownership resolution in machine learning","author":"Maini","year":"2021","journal-title":"arXiv:2104.10706"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01307"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1145\/3319535.3354211"},{"key":"ref35","first-page":"1","article-title":"ImageNet classification with deep convolutional neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"25","author":"Krizhevsky"},{"key":"ref36","article-title":"Towards deep learning models resistant to adversarial attacks","author":"Madry","year":"2017","journal-title":"arXiv:1706.06083"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-62144-5_4"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.2981604"},{"key":"ref39","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref40","first-page":"1","article-title":"A baseline for detecting misclassified and out-of-distribution examples in neural networks","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Hendrycks"},{"key":"ref41","first-page":"1","article-title":"Enhancing the reliability of out-of-distribution image detection in neural networks","volume-title":"Proc. 6th Int. Conf. Learn. Represent. (ICLR)","author":"Liang"},{"key":"ref42","first-page":"1","article-title":"A simple unified framework for detecting out-of-distribution samples and adversarial attacks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"31","author":"Lee"},{"key":"ref43","first-page":"21464","article-title":"Energy-based out-of-distribution detection","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Liu"},{"key":"ref44","article-title":"Generalized out-of-distribution detection: A survey","author":"Yang","year":"2021","journal-title":"arXiv:2110.11334"},{"key":"ref45","first-page":"144","article-title":"ReAct: Out-of-distribution detection with rectified activations","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Sun"},{"key":"ref46","first-page":"1","article-title":"Deep anomaly detection with outlier exposure","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Hendrycks"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref48","article-title":"Fashion-MNIST: A novel image dataset for benchmarking machine learning algorithms","author":"Xiao","year":"2017","journal-title":"arXiv:1708.07747"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10599-4_29"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2008.128"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2019.00031"},{"key":"ref52","article-title":"TABOR: A highly accurate approach to inspecting and restoring trojan backdoors in AI systems","author":"Guo","year":"2019","journal-title":"arXiv:1908.01763"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01617"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/52.43044"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2009.191"}],"container-title":["IEEE Transactions on Information Forensics and Security"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10206\/9970396\/10042038.pdf?arnumber=10042038","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T05:43:02Z","timestamp":1769492582000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10042038\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":55,"URL":"https:\/\/doi.org\/10.1109\/tifs.2023.3244107","relation":{},"ISSN":["1556-6013","1556-6021"],"issn-type":[{"value":"1556-6013","type":"print"},{"value":"1556-6021","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]}}}