{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T19:02:06Z","timestamp":1768071726414,"version":"3.49.0"},"reference-count":32,"publisher":"IEEE","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,10]]},"DOI":"10.1109\/milcom.2018.8599691","type":"proceedings-article","created":{"date-parts":[[2019,1,3]],"date-time":"2019-01-03T23:10:39Z","timestamp":1546557039000},"page":"547-552","source":"Crossref","is-referenced-by-count":15,"title":["Detecting Adversarial Examples Using Data Manifolds"],"prefix":"10.1109","author":[{"given":"Susmit","family":"Jha","sequence":"first","affiliation":[]},{"given":"Uyeong","family":"Jang","sequence":"additional","affiliation":[]},{"given":"Somesh","family":"Jha","sequence":"additional","affiliation":[]},{"given":"Brian","family":"Jalaian","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref32","article-title":"Feature squeezing: Detecting adversarial examples in deep neural networks","author":"weilin","year":"2017","journal-title":"ArXiv Preprint"},{"key":"ref31","doi-asserted-by":"crossref","first-page":"2319","DOI":"10.1126\/science.290.5500.2319","article-title":"A global geometric framework for nonlinear dimensionality reduction","volume":"290","author":"joshua","year":"2000","journal-title":"Science"},{"key":"ref30","article-title":"A boundary tilting persepective on the phenomenon of adversarial examples","author":"tanay","year":"2016","journal-title":"ArXiv Preprint"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1996.10476701"},{"key":"ref11","article-title":"Adversarial logit pairing","author":"kannan","year":"2018","journal-title":"ArXiv Preprint"},{"key":"ref12","article-title":"Adversarial examples for generative models","author":"kos","year":"2017","journal-title":"ArXiv Preprint"},{"key":"ref13","author":"alex","year":"2014","journal-title":"CIFAR-10 Dataset"},{"key":"ref14","article-title":"Adversarial examples in the physical world","author":"alexey","year":"2016","journal-title":"ArXiv Preprint"},{"key":"ref15","author":"lecun","year":"1998","journal-title":"The MNIST Database of Handwritten Digits"},{"key":"ref16","first-page":"2579","article-title":"Visualizing data using t-sne","volume":"9","author":"laurens van der","year":"2008","journal-title":"Journal of Machine Learning Research"},{"key":"ref17","article-title":"Towards deep learning models resistant to adversarial attacks","author":"aleksander","year":"2017","journal-title":"ArXiv Preprint"},{"key":"ref18","first-page":"135","article-title":"Magnet: a two-pronged defense against adversarial examples","author":"dongyu","year":"2017","journal-title":"Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security"},{"key":"ref19","article-title":"On detecting adversarial perturbations","author":"jan","year":"2017","journal-title":"ArXiv Preprint"},{"key":"ref28","article-title":"Pixeldefend: Leveraging generative models to understand and defend against adversarial examples","author":"yang","year":"2017","journal-title":"ArXiv Preprint"},{"key":"ref4","article-title":"Towards evaluating the robustness of neural networks","author":"nicholas","year":"2016","journal-title":"ArXiv Preprint"},{"key":"ref27","article-title":"Understanding adversarial training: Increasing local stability of neural nets through robust optimization","author":"uri","year":"2015","journal-title":"ArXiv Preprint"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CISS.2018.8362326"},{"key":"ref6","article-title":"Deep manifold traversal: Changing labels with convolutional features","author":"jacob","year":"2015","journal-title":"ar Xiv preprint"},{"key":"ref29","article-title":"Intriguing properties of neural networks","author":"christian","year":"2013","journal-title":"ArXiv Preprint"},{"key":"ref5","article-title":"Detecting adversarial samples from artifacts","author":"reuben","year":"2017","journal-title":"ArXiv Preprint"},{"key":"ref8","article-title":"On the (statistical) detection of adversarial examples","author":"grosse","year":"2017","journal-title":"ArXiv Preprint"},{"key":"ref7","article-title":"Explaining and harnessing adversarial examples (2014)","author":"ian","year":"0","journal-title":"ArXiv Preprint"},{"key":"ref2","first-page":"552","article-title":"Better mixing via deep representations","author":"bengio","year":"2013","journal-title":"International Conference on Machine Learning"},{"key":"ref9","article-title":"Distilling the knowledge in a neural network","author":"hinton","year":"2015","journal-title":"ArXiv Preprint"},{"key":"ref1","article-title":"Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples","author":"athalye","year":"2018","journal-title":"ArXiv Preprint"},{"key":"ref20","first-page":"2574","article-title":"Deepfool: a simple and accurate method to fool deep neural networks","author":"seyed-mohsen","year":"2016","journal-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition"},{"key":"ref22","first-page":"506","article-title":"Practical black-box attacks against machine learning","author":"nicolas","year":"2017","journal-title":"Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security"},{"key":"ref21","article-title":"cleverhans v2. 0.0: an adversarial machine learning library","author":"nicolas","year":"2016","journal-title":"ArXiv Preprint"},{"key":"ref24","first-page":"49","article-title":"Crafting adversarial input sequences for recurrent neural networks","author":"nicolas","year":"2016","journal-title":"IEEE Conference on Military Communications MILCOM"},{"key":"ref23","first-page":"372","article-title":"The limitations of deep learning in adversarial settings","author":"nicolas","year":"2016","journal-title":"2016 IEEE European Symposium on Security and Privacy (EuroS&P)"},{"key":"ref26","first-page":"119","article-title":"Think globally, fit locally: unsupervised learning of low dimensional manifolds","volume":"4","author":"lawrence","year":"2003","journal-title":"Journal of Machine Learning Research"},{"key":"ref25","doi-asserted-by":"crossref","first-page":"2323","DOI":"10.1126\/science.290.5500.2323","article-title":"Nonlinear dimensionality reduction by locally linear embedding","volume":"290","author":"sam","year":"2000","journal-title":"Science"}],"event":{"name":"MILCOM 2018 - IEEE Military Communications Conference","location":"Los Angeles, CA","start":{"date-parts":[[2018,10,29]]},"end":{"date-parts":[[2018,10,31]]}},"container-title":["MILCOM 2018 - 2018 IEEE Military Communications Conference (MILCOM)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8580348\/8599678\/08599691.pdf?arnumber=8599691","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,26]],"date-time":"2022-01-26T02:01:45Z","timestamp":1643162505000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8599691\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,10]]},"references-count":32,"URL":"https:\/\/doi.org\/10.1109\/milcom.2018.8599691","relation":{},"subject":[],"published":{"date-parts":[[2018,10]]}}}