{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T05:01:55Z","timestamp":1773378115640,"version":"3.50.1"},"reference-count":42,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,6,26]],"date-time":"2022-06-26T00:00:00Z","timestamp":1656201600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,6,26]],"date-time":"2022-06-26T00:00:00Z","timestamp":1656201600000},"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":[[2022,6,26]]},"DOI":"10.1109\/isit50566.2022.9834832","type":"proceedings-article","created":{"date-parts":[[2022,8,3]],"date-time":"2022-08-03T15:34:22Z","timestamp":1659540862000},"page":"3150-3155","source":"Crossref","is-referenced-by-count":4,"title":["Efficient and Robust Classification for Sparse Attacks"],"prefix":"10.1109","author":[{"given":"Mark","family":"Beliaev","sequence":"first","affiliation":[{"name":"Uni. of California Santa Barbara"}]},{"given":"Payam","family":"Delgosha","sequence":"additional","affiliation":[{"name":"Uni. of Illinois at Urbana-Champaign"}]},{"given":"Hamed","family":"Hassani","sequence":"additional","affiliation":[{"name":"Uni. of Pennsylvania"}]},{"given":"Ramtin","family":"Pedarsani","sequence":"additional","affiliation":[{"name":"Uni. of California Santa Barbara"}]}],"member":"263","reference":[{"key":"ref39","volume":"523","author":"huber","year":"2004","journal-title":"Robust Statistics"},{"key":"ref38","article-title":"Cifar-10 (canadian institute for advanced research)","author":"krizhevsky","year":"0"},{"key":"ref33","article-title":"Is bert really robust? natural language attack on text classification and entailment","volume":"2","author":"jin","year":"2019"},{"key":"ref32","article-title":"Adversarial perturbations against deep neural networks for malware classification","author":"grosse","year":"2016"},{"key":"ref31","first-page":"3896","article-title":"Adversarial camera stickers: A physical camera-based attack on deep learning systems","author":"li","year":"2019","journal-title":"International Conference on Machine Learning"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5888"},{"key":"ref37","article-title":"MNIST handwritten digit database","author":"lecun","year":"2010"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1137\/21M1426286"},{"key":"ref35","article-title":"Simple black-box adversarial perturbations for deep networks","author":"narodytska","year":"2016"},{"key":"ref34","article-title":"A simple explanation for the existence of adversarial examples with small hamming distance","author":"shamir","year":"2019"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00482"},{"key":"ref40","article-title":"Rectifier nonlinearities improve neural network acoustic models","author":"maas","year":"2013"},{"key":"ref11","article-title":"Intriguing properties of neural networks","author":"szegedy","year":"2013"},{"key":"ref12","author":"kurakin","year":"2016","journal-title":"Adversarial examples in the physical world"},{"key":"ref13","first-page":"274","article-title":"Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples","author":"athalye","year":"2018","journal-title":"International Conference on Machine Learning"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ISIT.2018.8437638"},{"key":"ref15","article-title":"Improving adversarial robustness via channel-wise activation suppressing","author":"bai","year":"2020","journal-title":"International Conference on Learning Representations"},{"key":"ref16","first-page":"2574","article-title":"Deepfool: a simple and accurate method to fool deep neural networks","author":"moosavi-dezfooli","year":"2016","journal-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00445"},{"key":"ref18","article-title":"Enhancing adversarial defense by k-winners-take-all","author":"xiao","year":"2020","journal-title":"International Conference on Learning Representations"},{"key":"ref19","first-page":"8","article-title":"Defensive quantization: When efficiency meets robustness","author":"lin","year":"2019","journal-title":"Artificial Intelligence Communication Imaging Navigation Sensing Systems"},{"key":"ref28","article-title":"Towards the first adversarially robust neural network model on mnist","author":"schott","year":"2018"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1038\/nature16961"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/EuroSP.2016.36"},{"key":"ref3","article-title":"Playing atari with deep reinforcement learning","author":"mnih","year":"2013"},{"key":"ref6","article-title":"Intriguing properties of neural networks","author":"szegedy","year":"2013"},{"key":"ref29","article-title":"sparse-rs: a versatile framework for query-efficient sparse black-box adversarial attacks","author":"croce","year":"2020"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-40994-3_25"},{"key":"ref8","article-title":"Towards deep learning models resistant to adversarial attacks","author":"madry","year":"2017"},{"key":"ref7","article-title":"Explaining and harnessing adversarial examples","author":"goodfellow","year":"2014"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P16-1231"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.49"},{"key":"ref1","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 1 NIPS’12"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11302"},{"key":"ref22","article-title":"Robustness may be at odds with accuracy","author":"tsipras","year":"2018"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00930"},{"key":"ref42","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2014"},{"key":"ref24","article-title":"Adversarial training can hurt generalization","author":"raghunathan","year":"2019"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/ISIT50566.2022.9834832"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01258-8_39"},{"key":"ref26","first-page":"2034","article-title":"Precise tradeoffs in adversarial training for linear regression","author":"javanmard","year":"2020","journal-title":"Conference on Learning Theory"},{"key":"ref25","first-page":"7472","article-title":"Theoretically principled trade-off between robustness and accuracy","author":"zhang","year":"2019","journal-title":"International Conference on Machine Learning"}],"event":{"name":"2022 IEEE International Symposium on Information Theory (ISIT)","location":"Espoo, Finland","start":{"date-parts":[[2022,6,26]]},"end":{"date-parts":[[2022,7,1]]}},"container-title":["2022 IEEE International Symposium on Information Theory (ISIT)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9834325\/9834269\/09834832.pdf?arnumber=9834832","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T20:36:22Z","timestamp":1773347782000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9834832\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,26]]},"references-count":42,"URL":"https:\/\/doi.org\/10.1109\/isit50566.2022.9834832","relation":{},"subject":[],"published":{"date-parts":[[2022,6,26]]}}}