{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,23]],"date-time":"2024-10-23T02:22:37Z","timestamp":1729650157692,"version":"3.28.0"},"reference-count":59,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022,1]]},"DOI":"10.1109\/wacv51458.2022.00287","type":"proceedings-article","created":{"date-parts":[[2022,2,15]],"date-time":"2022-02-15T20:56:28Z","timestamp":1644958588000},"page":"2815-2825","source":"Crossref","is-referenced-by-count":4,"title":["Attack Agnostic Detection of Adversarial Examples via Random Subspace Analysis"],"prefix":"10.1109","author":[{"given":"Nathan","family":"Drenkow","sequence":"first","affiliation":[{"name":"The Johns Hopkins University Applied Physics Laboratory,Laurel,MD,USA,20723"}]},{"given":"Neil","family":"Fendley","sequence":"additional","affiliation":[{"name":"The Johns Hopkins University Applied Physics Laboratory,Laurel,MD,USA,20723"}]},{"given":"Philippe","family":"Burlina","sequence":"additional","affiliation":[{"name":"The Johns Hopkins University Applied Physics Laboratory,Laurel,MD,USA,20723"}]}],"member":"263","reference":[{"key":"ref39","article-title":"On detecting adversarial perturbations","author":"metzen","year":"2017","journal-title":"arXiv preprint arXiv 1702 04710"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-45726-7_19"},{"key":"ref33","article-title":"Fastened crown: Tightened neural network robustness certificates","author":"lyu","year":"2019","journal-title":"arXiv preprint arXiv 1912 00574"},{"key":"ref32","article-title":"Detecting adversarial image examples in deep neural networks with adaptive noise reduction","author":"liang","year":"2018","journal-title":"IEEE Transactions on Dependable and Secure Computing"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.615"},{"key":"ref30","article-title":"Wasserstein smoothing: Certified robustness against wasserstein adversarial attacks","author":"levine","year":"2019","journal-title":"arXiv preprint arXiv 1910 10335"},{"key":"ref37","article-title":"Towards deep learning models resistant to adversarial attacks","author":"madry","year":"2017","journal-title":"arXiv preprint arXiv 1706 06083"},{"key":"ref36","article-title":"Characterizing adversarial sub-spaces using local intrinsic dimensionality","author":"ma","year":"2018","journal-title":"arXiv preprint arXiv 1801 02929"},{"key":"ref35","article-title":"Combinatorial testing for deep learning systems","author":"ma","year":"2018","journal-title":"arXiv preprint arXiv 1806 07723"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1145\/3238147.3238202"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2019.00044"},{"key":"ref27","article-title":"Curse of dimensionality on randomized smoothing for certifiable robustness","author":"kumar","year":"2020","journal-title":"arXiv preprint arXiv 2002 05155"},{"key":"ref29","first-page":"7167","article-title":"A simple unified framework for detecting out-of-distribution samples and adversarial attacks","author":"lee","year":"2018","journal-title":"Advances in neural information processing systems"},{"key":"ref2","article-title":"Training ensembles to detect adversarial examples","author":"bagnali","year":"2017","journal-title":"arXiv preprint arXiv 1712 04006"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2018.2876048"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01096"},{"key":"ref22","first-page":"125","article-title":"Adversarial examples are not bugs, they are features","author":"ilyas","year":"2019","journal-title":"Advances in neural information processing systems"},{"key":"ref21","first-page":"4700","article-title":"Densely connected convolutional networks","author":"huang","year":"2017","journal-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition"},{"key":"ref24","article-title":"Unsupervised detection of adversarial examples with model explanations","author":"ko","year":"2021","journal-title":"arXiv preprint arXiv 2107 10480"},{"key":"ref23","first-page":"1","article-title":"Extensions of lipschitz mappings into a hilbert space","volume":"26","author":"johnson","year":"1984","journal-title":"Contemporary Mathematics"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1137\/100810447"},{"key":"ref25","article-title":"Oodformer: Out-of-distribution detection transformer","author":"koner","year":"2021","journal-title":"arXiv preprint arXiv 2107 08976"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1162\/089976601750264965"},{"key":"ref51","article-title":"Robustness certificates against adversarial examples for relu networks","author":"singla","year":"2019","journal-title":"arXiv preprint arXiv 1902 10869"},{"key":"ref59","article-title":"Feature squeezing: Detecting adversarial examples in deep neural networks","author":"xu","year":"2017","journal-title":"arXiv preprint arXiv 1704 01155"},{"key":"ref58","first-page":"5286","article-title":"Provable defenses against adversarial examples via the convex outer adversarial polytope","author":"wong","year":"2018","journal-title":"International Conference on Machine Learning"},{"key":"ref57","first-page":"5276","article-title":"Towards fast computation of certified robustness for relu networks","author":"weng","year":"2018","journal-title":"International Conference on Machine Learning"},{"key":"ref56","first-page":"6727","article-title":"Proven: Verifying robustness of neural networks with a probabilistic approach","author":"weng","year":"2019","journal-title":"International Conference on Machine Learning"},{"key":"ref55","article-title":"Evaluating the robustness of nearest neighbor classifiers: A primal-dual perspective","author":"wang","year":"2019","journal-title":"arXiv preprint arXiv 1906 03008"},{"key":"ref54","doi-asserted-by":"crossref","DOI":"10.1609\/aaai.v32i1.11828","article-title":"Detecting adversarial examples through image transformation","author":"tian","year":"2018","journal-title":"Thirty-Second AAAI Conference on Artificial Intelligence"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1145\/3238147.3238172"},{"key":"ref52","article-title":"Testing deep neural networks","author":"sun","year":"2018","journal-title":"arXiv preprint arXiv 1803 04792"},{"key":"ref10","article-title":"Experiments with random projection","author":"dasgupta","year":"2013","journal-title":"arXiv preprint arXiv 1301 3849"},{"key":"ref40","article-title":"When not to classify: Anomaly detection of attacks (ada) on dnn classifiers at test time","author":"miller","year":"2017","journal-title":"arXiv preprint arXiv 1712 06646"},{"key":"ref11","first-page":"1","article-title":"An elementary proof of the johnson-lindenstrauss lemma","volume":"22","author":"dasgupta","year":"1999","journal-title":"International Computer Science Institute Technical Report"},{"key":"ref12","article-title":"Detecting adversarial samples from artifacts","author":"feinman","year":"2017","journal-title":"arXiv preprint arXiv 1703 00410"},{"key":"ref13","article-title":"When explainability meets adversarial learning: Detecting adversarial examples using shap signatures","author":"fidel","year":"2019","journal-title":"arXiv preprint arXiv 1909 01771"},{"key":"ref14","article-title":"Exploring the limits of out-of-distribution detection","author":"fort","year":"2021","journal-title":"arXiv preprint arXiv 2106 01111"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2018.00058"},{"key":"ref16","article-title":"Explaining and harnessing adversarial examples","author":"goodfellow","year":"2014","journal-title":"arXiv preprint arXiv 1412 6572"},{"key":"ref17","article-title":"On the (statistical) detection of adversarial examples","author":"grosse","year":"2017","journal-title":"arXiv preprint arXiv 1702 06280"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref19","first-page":"43","article-title":"Context vectors: general purpose approximate meaning representations self-organized from raw data","volume":"3","author":"hecht-nielsen","year":"1994","journal-title":"Computational Intelligence Imitating Life"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33013240"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1145\/502512.502546"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/3128572.3140444"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3010274"},{"key":"ref8","article-title":"Ead: elastic-net attacks to deep neural networks via adversarial examples","author":"chen","year":"2017","journal-title":"arXiv preprint arXiv 1709 04396"},{"key":"ref49","first-page":"5498","article-title":"The odds are odd: A statistical test for detecting adversarial examples","author":"roth","year":"2019","journal-title":"International Conference on Machine Learning"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.49"},{"key":"ref9","first-page":"483","article-title":"Detecting adversarial examples through nonlinear dimensionality reduction","author":"crecchi","year":"2019","journal-title":"27th European Symposium on Artificial Neural Networks Computational Intelligence and Machine Learning ESANN 2019"},{"journal-title":"Detecting out-of-distribution samples using low-order deep features statistics","year":"2018","author":"quintanilha","key":"ref46"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1145\/3132747.3132785"},{"key":"ref48","article-title":"Fool-box: A python toolbox to benchmark the robustness of machine learning models","author":"rauber","year":"2017","journal-title":"arXiv preprint arXiv 1707 04555"},{"key":"ref47","article-title":"Certified defenses against adversarial examples","author":"raghunathan","year":"2018","journal-title":"arXiv preprint arXiv 1801 09344"},{"key":"ref42","article-title":"Out-of-distribution detection with subspace techniques and probabilistic modeling of features","author":"ndiour","year":"2020","journal-title":"2012 arXiv preprint arXiv"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5966"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/EuroSP.2016.36"},{"key":"ref43","article-title":"Deep k-nearest neighbors: Towards confident, interpretable and robust deep learning","author":"papernot","year":"2018","journal-title":"arXiv preprint arXiv 1803 04765"}],"event":{"name":"2022 IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV)","start":{"date-parts":[[2022,1,3]]},"location":"Waikoloa, HI, USA","end":{"date-parts":[[2022,1,8]]}},"container-title":["2022 IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9706406\/9706408\/09706691.pdf?arnumber=9706691","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,27]],"date-time":"2023-01-27T01:09:45Z","timestamp":1674781785000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9706691\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1]]},"references-count":59,"URL":"https:\/\/doi.org\/10.1109\/wacv51458.2022.00287","relation":{},"subject":[],"published":{"date-parts":[[2022,1]]}}}