{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,6]],"date-time":"2025-12-06T04:44:53Z","timestamp":1764996293012},"reference-count":37,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,1,10]],"date-time":"2021-01-10T00:00:00Z","timestamp":1610236800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,1,10]],"date-time":"2021-01-10T00:00:00Z","timestamp":1610236800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,1,10]],"date-time":"2021-01-10T00:00:00Z","timestamp":1610236800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,1,10]]},"DOI":"10.1109\/icpr48806.2021.9412385","type":"proceedings-article","created":{"date-parts":[[2021,5,5]],"date-time":"2021-05-05T22:15:54Z","timestamp":1620252954000},"page":"7587-7594","source":"Crossref","is-referenced-by-count":3,"title":["Adaptive Noise Injection for Training Stochastic Student Networks from Deterministic Teachers"],"prefix":"10.1109","author":[{"given":"Yi Xianz","family":"Marcus Tan","sequence":"first","affiliation":[]},{"given":"Yuval","family":"Elovici","sequence":"additional","affiliation":[]},{"given":"Alexander","family":"Binder","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref33","article-title":"Learning multiple layers of features from tiny images","author":"krizhevsky","year":"2009","journal-title":"Tech Rep"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.49"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00957"},{"key":"ref37","first-page":"284","article-title":"Synthesizing robust adversarial examples","author":"athalye","year":"0","journal-title":"International Conference on Machine Learning"},{"key":"ref36","first-page":"1281","article-title":"With great training comes great vulnerability: Practical attacks against transfer learning","author":"wang","year":"0","journal-title":"27th USENIX Security Symposium ( USENIX Security 18)"},{"key":"ref35","article-title":"Foolbox: A python toolbox to benchmark the robustness of machine learning models","author":"rauber","year":"2017","journal-title":"arXiv preprint arXiv 1707 03374"},{"journal-title":"Automatic differentiation in pytorch","year":"2017","author":"paszke","key":"ref34"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00132"},{"key":"ref11","article-title":"Theoretically principled trade-off between robustness and accuracy","author":"zhang","year":"2019","journal-title":"arXiv preprint arXiv 1901 04668"},{"key":"ref12","first-page":"4970","article-title":"Improving adversarial robustness via promoting ensemble diversity","author":"pang","year":"0","journal-title":"International Conference on Machine Learning"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/833"},{"key":"ref14","first-page":"2712","article-title":"Using pre-training can improve model robustness and uncertainty","author":"hendrycks","year":"0","journal-title":"International Conference on Machine Learning"},{"key":"ref15","article-title":"Towards deep learning models resistant to adversarial attacks","author":"madry","year":"0","journal-title":"International Conference on Learning Representations"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00068"},{"key":"ref17","article-title":"Robustness may be at odds with accuracy","author":"tsipras","year":"2018","journal-title":"arXiv preprint arXiv 1805 12152"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00348"},{"key":"ref19","article-title":"Explaining and harnessing adversarial examples","author":"goodfellow","year":"2014","journal-title":"arXiv preprint arXiv 1412 6572"},{"key":"ref28","article-title":"Decision-based adversarial attacks: Reliable attacks against black-box machine learning models","author":"brendel","year":"0","journal-title":"International Conference on Learning Representations"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/SPW.2018.00011"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1145\/3052973.3053009"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2012.2205597"},{"key":"ref6","article-title":"Intriguing properties of neural networks","author":"szegedy","year":"2013","journal-title":"arXiv preprint arXiv 1312 6199"},{"key":"ref29","article-title":"Adversarial examples in the physical world","author":"kurakin","year":"2016","journal-title":"arXiv preprint arXiv 1607 02533"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/HASE.2017.36"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/651"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00496"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2644615"},{"key":"ref9","article-title":"Feature squeezing: Detecting adversarial examples in deep neural networks","author":"xu","year":"2017","journal-title":"arXiv preprint arXiv 1704 01155"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.683"},{"key":"ref20","article-title":"n-ml: Mitigating adversarial examples via ensembles of topologically manipulated classifiers","author":"sharif","year":"2019","journal-title":"arXiv preprint arXiv 1912 09059"},{"key":"ref22","first-page":"11 838","article-title":"Theoretical evidence for adversarial robustness through randomization","author":"pinot","year":"2019","journal-title":"Advances in neural information processing systems"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2019.00044"},{"key":"ref24","article-title":"Estimating or propagating gradients through stochastic neurons for conditional computation","author":"bengio","year":"2013","journal-title":"arXiv preprint arXiv 1308 3432"},{"key":"ref23","first-page":"4107","article-title":"Bi-narized neural networks","author":"hubara","year":"2016","journal-title":"Advances in neural information processing systems"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/SP40000.2020.00045"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00444"}],"event":{"name":"2020 25th International Conference on Pattern Recognition (ICPR)","start":{"date-parts":[[2021,1,10]]},"location":"Milan, Italy","end":{"date-parts":[[2021,1,15]]}},"container-title":["2020 25th International Conference on Pattern Recognition (ICPR)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9411940\/9411911\/09412385.pdf?arnumber=9412385","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T11:40:52Z","timestamp":1652182852000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9412385\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,10]]},"references-count":37,"URL":"https:\/\/doi.org\/10.1109\/icpr48806.2021.9412385","relation":{},"subject":[],"published":{"date-parts":[[2021,1,10]]}}}