{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T15:42:49Z","timestamp":1783698169661,"version":"3.55.0"},"reference-count":54,"publisher":"IEEE","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,7]]},"DOI":"10.23919\/icif.2018.8455710","type":"proceedings-article","created":{"date-parts":[[2018,9,6]],"date-time":"2018-09-06T22:47:48Z","timestamp":1536274068000},"page":"838-845","source":"Crossref","is-referenced-by-count":21,"title":["Why the Failure? How Adversarial Examples Can Provide Insights for Interpretable Machine Learning"],"prefix":"10.23919","author":[{"given":"Richard","family":"Tomsett","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amy","family":"Widdicombe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianwei","family":"Xing","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Supriyo","family":"Chakraborty","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Simon","family":"Julier","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Prudhvi","family":"Gurram","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Raghuveer","family":"Rao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mani","family":"Srivastava","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref39","author":"alzantot","year":"2018","journal-title":"Did you hear that? adversarial examples against automatic speech recognition"},{"key":"ref38","year":"0","journal-title":"Microsoft Computer Vision API Demo"},{"key":"ref33","author":"brown","year":"2017","journal-title":"Adversarial Patch"},{"key":"ref32","author":"sitawarin","year":"2018","journal-title":"Rogue signs Deceiving traffic sign recognition with malicious ads and logos"},{"key":"ref31","author":"evtimov","year":"2017","journal-title":"Robust physical-world attacks on deep learning models"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/525"},{"key":"ref37","year":"0","journal-title":"IBM Watson Visual Recognition API Demo"},{"key":"ref36","year":"0","journal-title":"Google Vision API Demo"},{"key":"ref35","author":"lu","year":"2017","journal-title":"Standard detectors aren't (currently) fooled by physical adversarial stop signs"},{"key":"ref34","author":"athalye","year":"2017","journal-title":"Synthesizing robust adversarial examples"},{"key":"ref28","author":"koh","year":"2017","journal-title":"Understanding black-box predictions via influence functions"},{"key":"ref27","author":"goodfellow","year":"2014","journal-title":"Explaining and Harnessing Adversarial Examples"},{"key":"ref29","author":"kos","year":"2017","journal-title":"Adversarial examples for generative models"},{"key":"ref2","first-page":"1","article-title":"Deep Learning for Situational Understanding","author":"chakraborty","year":"0","journal-title":"Information Fusion (FUSION) 2017 20th International Conference on"},{"key":"ref1","author":"goodfellow","year":"2016","journal-title":"Deep Learning"},{"key":"ref20","first-page":"1321","article-title":"On calibration of modern neural networks","volume":"70","author":"guo","year":"2017","journal-title":"Proceedings of the 34th International Conference on Machine Learning ser Proceedings of Machine Learning Research"},{"key":"ref22","author":"rasmussen","year":"2006","journal-title":"Gaussian Processes for Machine Learning"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/1102351.1102430"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1145\/1014052.1014066"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1353\/pbm.2017.0000"},{"key":"ref26","author":"biggio","year":"2017","journal-title":"Wild patterns Ten years after the rise of adversarial machine learning"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1145\/1081870.1081950"},{"key":"ref50","author":"carlini","year":"2016","journal-title":"Towards Evaluating the Robustness of Neural Networks"},{"key":"ref51","article-title":"The (un) reliability of saliency methods","volume":"absi1711 867","author":"kindermans","year":"2017","journal-title":"CoRR"},{"key":"ref54","author":"bradshaw","year":"2017","journal-title":"Adversarial examples uncertainty and transfer testing robustness in Gaussian process hybrid deep networks"},{"key":"ref53","article-title":"Harnessing model uncertainty for detecting adversarial examples","author":"rawat","year":"2017","journal-title":"Workshop on Bayesian Deep Learning NIPS"},{"key":"ref52","author":"grosse","year":"2017","journal-title":"How wrong am I? Studying adversarial examples and their impact on uncertainty in gaussian process machine learning models"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/UIC-ATC.2017.8397411"},{"key":"ref11","author":"miller","year":"2017","journal-title":"Explanation in Artificial Intelligence Insights from the Social Sciences"},{"key":"ref40","first-page":"644","article-title":"Secure and resilient distributed machine learning under adversarial environments","author":"zhang","year":"2015","journal-title":"Information Fusion (FUSION) 2015 18th International Conference on"},{"key":"ref12","article-title":"The mythos of model interpretability","volume":"absi1606 3490","author":"lipton","year":"2016","journal-title":"CoRR"},{"key":"ref13","article-title":"Integrating learning and reasoning services for explainable information fusion","author":"harborne","year":"2018","journal-title":"International Journal of Pattern Recognition and Artificial Intelligence"},{"key":"ref14","author":"doshi-velez","year":"2017","journal-title":"Towards a rigorous science of interpretable machine learning"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref16","author":"krizhevsky","year":"2009","journal-title":"Learning multiple layers of features from tiny images"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref18","author":"dhurandhar","year":"2017","journal-title":"A formal framework to characterize interpretability of procedures"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2007.4408844"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ICMCIS.2016.7496574"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2016.355"},{"key":"ref6","author":"yuan","year":"2017","journal-title":"Adversarial examples Attacks and defenses for deep learning"},{"key":"ref5","author":"szegedy","year":"2013","journal-title":"Intriguing properties of neural networks"},{"key":"ref8","doi-asserted-by":"crossref","DOI":"10.23919\/ICIF.2017.8009785","article-title":"Deep learning for situational understanding","author":"chakraborty","year":"2017","journal-title":"Information Fusion (FUSION) 2017 20th International Conference on"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/2046684.2046692"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2016.11.008"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1518\/001872095779049543"},{"key":"ref46","author":"simonyan","year":"2013","journal-title":"Deep Inside Convolutional Networks Visualising Image Classification Models and Saliency Maps"},{"key":"ref45","author":"ross","year":"2017","journal-title":"Improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939778"},{"key":"ref47","article-title":"Layer-wise relevance propagation for neural networks with local renormalization layers","volume":"abs 1604 825","author":"binder","year":"2016","journal-title":"CoRR"},{"key":"ref42","author":"chen","year":"2017","journal-title":"Distributed statistical machine learning in adversarial settings Byzantine gradient descent"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/CISS.2017.7926118"},{"key":"ref44","author":"dong","year":"2017","journal-title":"Towards interpretable deep neural networks by leveraging adversarial examples"},{"key":"ref43","first-page":"2940","article-title":"Cognitive psychology for deep neural networks: A shape bias case study","volume":"70","author":"ritter","year":"2017","journal-title":"Proceedings of the 34th International Conference on Machine Learning ser Proceedings of Machine Learning Research"}],"event":{"name":"2018 International Conference on Information Fusion (FUSION)","location":"Cambridge, United Kingdom","start":{"date-parts":[[2018,7,10]]},"end":{"date-parts":[[2018,7,13]]}},"container-title":["2018 21st International Conference on Information Fusion (FUSION)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8442112\/8454975\/08455710.pdf?arnumber=8455710","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,26]],"date-time":"2022-01-26T10:16:12Z","timestamp":1643192172000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8455710\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,7]]},"references-count":54,"URL":"https:\/\/doi.org\/10.23919\/icif.2018.8455710","relation":{},"subject":[],"published":{"date-parts":[[2018,7]]}}}