{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T19:29:26Z","timestamp":1768678166049,"version":"3.49.0"},"reference-count":17,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T00:00:00Z","timestamp":1658102400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T00:00:00Z","timestamp":1658102400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100012365","name":"Howard University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100012365","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100016311","name":"Arm","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100016311","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012365","name":"Howard University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100012365","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,7,18]]},"DOI":"10.23919\/annsim55834.2022.9859462","type":"proceedings-article","created":{"date-parts":[[2022,8,23]],"date-time":"2022-08-23T19:52:45Z","timestamp":1661284365000},"page":"246-257","source":"Crossref","is-referenced-by-count":2,"title":["Adversarial Machine Learning Using Convolutional Neural Network With Imagenet"],"prefix":"10.23919","author":[{"given":"Utsab","family":"Khakurel","sequence":"first","affiliation":[{"name":"Howard University,Department of Electrical Engineering and Computer Science,Washington DC,USA,20059"}]},{"given":"Danda B.","family":"Rawat","sequence":"additional","affiliation":[{"name":"Howard University,Department of Electrical Engineering and Computer Science,Washington DC,USA,20059"}]}],"member":"263","reference":[{"key":"ref10","article-title":"ML Attack Models: Adversarial Attacks and Data Poisoning Attacks","volume":"abs 2112 2797","author":"lin","year":"2021","journal-title":"ArXiv"},{"key":"ref11","article-title":"Using a GAN to Generate Adversarial Examples to Facial Image Recognition","volume":"abs 2111 15213","author":"merrigan","year":"2021","journal-title":"ArXiv"},{"key":"ref12","article-title":"Practical Black-Box Attacks against Deep Learning Systems using Adversarial Examples","volume":"abs 1602 2697","author":"papernot","year":"2016","journal-title":"ArXiv"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2019.2890858"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3130903"},{"key":"ref15","article-title":"Intriguing properties of neural networks","volume":"abs 1312 6199","author":"szegedy","year":"2014","journal-title":"ArXiv"},{"key":"ref16","article-title":"Understanding the One-Pixel Attack: Propagation Maps and Locality Analysis","volume":"abs 1902 2947","author":"vargas","year":"2019","journal-title":"ArXiv"},{"key":"ref17","article-title":"Mitigating Adversarial Attacks by Distributing Different Copies to Different Users","volume":"abs 2111 15160","author":"zhang","year":"2021","journal-title":"ArXiv"},{"key":"ref4","article-title":"Adversarial Patch","volume":"abs 1712 9665","author":"brown","year":"2017","journal-title":"ArXiv"},{"key":"ref3","author":"banks","year":"2000","journal-title":"Discrete-Event System Simulation"},{"key":"ref6","article-title":"Adversarial Attacks in Cooperative AI","volume":"abs 2111 14833","author":"fujimoto","year":"2021","journal-title":"ArXiv"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref8","article-title":"Towards Deep Neural Network Architectures Robust to Adversarial Examples","author":"gu","year":"2015","journal-title":"3rd International Conference on Learning Representations ICLR 2015 San Diego CA USA May 7-9 2015 Workshop Track Proceedings"},{"key":"ref7","article-title":"Explaining and Harnessing Adversarial! Examples","author":"goodfellow","year":"2015","journal-title":"3rd International Conference on Learning Representations ICLR 2015 San Diego CA USA May 7-9 2015 Conference Track Proceedings"},{"key":"ref2","first-page":"284","article-title":"Synthesizing Robust Adversarial Examples","author":"athalye","year":"2018","journal-title":"Proceedings of the 35th International Conference on Machine Learning Volume 80 of Proceedings of Machine Learning Research"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115782"},{"key":"ref9","article-title":"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications","volume":"abs 1704 4861","author":"howard","year":"2017","journal-title":"ArXiv"}],"event":{"name":"2022 Annual Modeling and Simulation Conference (ANNSIM)","location":"San Diego, CA, USA","start":{"date-parts":[[2022,7,18]]},"end":{"date-parts":[[2022,7,20]]}},"container-title":["2022 Annual Modeling and Simulation Conference (ANNSIM)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9859263\/9859270\/09859462.pdf?arnumber=9859462","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,12]],"date-time":"2022-09-12T20:02:29Z","timestamp":1663012949000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9859462\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,18]]},"references-count":17,"URL":"https:\/\/doi.org\/10.23919\/annsim55834.2022.9859462","relation":{},"subject":[],"published":{"date-parts":[[2022,7,18]]}}}