{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T05:07:04Z","timestamp":1783746424770,"version":"3.55.0"},"reference-count":35,"publisher":"IEEE","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,10]]},"DOI":"10.1109\/btas.2018.8698567","type":"proceedings-article","created":{"date-parts":[[2019,4,25]],"date-time":"2019-04-25T23:48:37Z","timestamp":1556236117000},"page":"1-7","source":"Crossref","is-referenced-by-count":42,"title":["SmartBox: Benchmarking Adversarial Detection and Mitigation Algorithms for Face Recognition"],"prefix":"10.1109","author":[{"given":"Akhil","family":"Goel","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anirudh","family":"Singh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akshay","family":"Agarwal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mayank","family":"Vatsa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Richa","family":"Singh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref33","article-title":"Intriguing properties of neural networks","author":"szegedy","year":"2014","journal-title":"ICLRE"},{"key":"ref32","article-title":"One pixel attack for fooling deep neural networks","volume":"abs 1710 8864","author":"su","year":"2017","journal-title":"CoRR"},{"key":"ref31","article-title":"Adversarial generative nets: Neural network attacks on state-of-the-art face recognition","author":"sharif","year":"2017","journal-title":"arXiv preprint arXiv 1801 04144"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978392"},{"key":"ref35","article-title":"Mitigating adversarial effects through randomization","author":"xie","year":"2018","journal-title":"ICLRE"},{"key":"ref34","article-title":"Learning adversary-resistant deep neural networks","author":"wang","year":"2016","journal-title":"arXiv preprint arXiv 1612 01401"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00957"},{"key":"ref11","article-title":"Detecting adversarial samples from artifacts","author":"feinman","year":"2017","journal-title":"arXiv preprint arXiv 1703 00410"},{"key":"ref12","article-title":"Deepcloak: Masking deep neural network models for robustness against adversarial samples","author":"gao","year":"2017","journal-title":"ICLRE"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/34.927464"},{"key":"ref14","article-title":"Adversarial and clean data are not twins","author":"gong","year":"2017","journal-title":"arXiv preprint arXiv 1704 04960"},{"key":"ref15","article-title":"Explaining and harnessing adversarial examples","author":"goodfellow","year":"2014","journal-title":"arXiv preprint arXiv 1412 6572"},{"key":"ref16","article-title":"Unravelling robustness of deep learning based face recognition against adversarial attacks","author":"goswami","year":"2018","journal-title":"AAAI"},{"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\/ICCV.2015.123"},{"key":"ref19","article-title":"On Detecting Adversarial Perturbations","author":"hendrik metzen","year":"2017","journal-title":"ICLRE"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.282"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/3128572.3140444"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.17"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CISS.2018.8362326"},{"key":"ref6","article-title":"EAD: elastic-net attacks to deep neural networks via adversarial examples","author":"chen","year":"2018","journal-title":"AAAI"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2016.41"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.49"},{"key":"ref8","article-title":"Denoising adversarial autoencoders","author":"creswell","year":"2017","journal-title":"CoRR abs\/1703 01220"},{"key":"ref7","first-page":"854","article-title":"Parseval networks: Improving robustness to adversarial examples","author":"cisse","year":"2017","journal-title":"ICML"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2807385"},{"key":"ref9","article-title":"Keeping the bad guys out: Protecting and vaccinating deep learning with jpeg compression","author":"das","year":"2017","journal-title":"arXiv preprint arXiv 1705 02900"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/BTAS.2018.8698548"},{"key":"ref20","article-title":"Visible progress on adversarial images and a new saliency map","volume":"abs 1608 530","author":"hendrycks","year":"2016","journal-title":"CoRR"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.615"},{"key":"ref21","doi-asserted-by":"crossref","first-page":"684","DOI":"10.1109\/TPAMI.2005.92","article-title":"Acquiring linear sub-spaces for face recognition under variable lighting","volume":"27","author":"lee","year":"2005","journal-title":"TPAMI"},{"key":"ref24","first-page":"3811","article-title":"Surpassing human-level face verification performance on lfw with gaussianface","author":"lu","year":"2015","journal-title":"AAAI"},{"key":"ref23","article-title":"Detecting adversarial examples in deep networks with adaptive noise reduction","author":"liang","year":"2017","journal-title":"arXiv preprint arXiv 1705 08378"},{"key":"ref26","article-title":"Towards deep learning models resistant to adversarial attacks","author":"madry","year":"2017","journal-title":"arXiv preprint arXiv 1706 06083"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.56"}],"event":{"name":"2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS)","location":"Redondo Beach, CA, USA","start":{"date-parts":[[2018,10,22]]},"end":{"date-parts":[[2018,10,25]]}},"container-title":["2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8692576\/8698537\/08698567.pdf?arnumber=8698567","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,5,13]],"date-time":"2019-05-13T19:09:32Z","timestamp":1557774572000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8698567\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,10]]},"references-count":35,"URL":"https:\/\/doi.org\/10.1109\/btas.2018.8698567","relation":{},"subject":[],"published":{"date-parts":[[2018,10]]}}}