{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T01:25:48Z","timestamp":1740101148922,"version":"3.37.3"},"reference-count":49,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,8,21]],"date-time":"2022-08-21T00:00:00Z","timestamp":1661040000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,8,21]],"date-time":"2022-08-21T00:00:00Z","timestamp":1661040000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100012389","name":"National Institute of Information and Communications Technology","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012389","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004721","name":"University of Tokyo","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004721","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,8,21]]},"DOI":"10.1109\/icpr56361.2022.9956572","type":"proceedings-article","created":{"date-parts":[[2022,11,29]],"date-time":"2022-11-29T19:34:13Z","timestamp":1669750453000},"page":"2279-2285","source":"Crossref","is-referenced-by-count":0,"title":["Pixel to Binary Embedding Towards Robustness for CNNs"],"prefix":"10.1109","author":[{"given":"Ikki","family":"Kishida","sequence":"first","affiliation":[{"name":"The University of Tokyo,Tokyo,Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hideki","family":"Nakayama","sequence":"additional","affiliation":[{"name":"The University of Tokyo,Tokyo,Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","article-title":"Robustness may be at odds with accuracy","author":"tsipras","year":"2019","journal-title":"ICLRE"},{"key":"ref38","article-title":"Intriguing properties of neural networks","author":"szegedy","year":"2014","journal-title":"ICLRE"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58580-8_4"},{"key":"ref32","article-title":"Xnor-net: Imagenet classification using binary convolutional neural networks","author":"rastegari","year":"2016","journal-title":"ECCV"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.282"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.17"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2019.2890858"},{"key":"ref36","article-title":"Pixelde-fend: Leveraging generative models to understand and defend against adversarial examples","author":"song","year":"2018","journal-title":"ICLRE"},{"key":"ref35","article-title":"Robust local features for improving the generalization of adversarial training","author":"song","year":"2020","journal-title":"ICLRE"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"ref28","article-title":"Towards deep learning models resistant to adversarial attacks","author":"madry","year":"2018","journal-title":"ICLRE"},{"key":"ref27","article-title":"Characterizing adversarial subspaces using local intrinsic dimensionality","author":"ma","year":"2018","journal-title":"ICLRE"},{"key":"ref29","article-title":"Benchmarking robustness in object detection: Autonomous driving when winter is coming","author":"michaelis","year":"2019","journal-title":"CoRR"},{"key":"ref2","article-title":"Estimating or propagating gradients through stochastic neurons for conditional computation","author":"bengio","year":"2013","journal-title":"CoRR"},{"key":"ref1","article-title":"Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples","author":"athalye","year":"2018","journal-title":"ICML"},{"key":"ref20","article-title":"Imagenet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"NIPS"},{"key":"ref22","article-title":"Compounding the performance improvements of assembled techniques in a convolutional neural network","author":"lee","year":"2020","journal-title":"CoRR"},{"key":"ref21","article-title":"Adversarial attacks and defences competition","author":"kurakin","year":"2017","journal-title":"The NIPS ’17 Competition Building Intelligent Systems The Springer Series on Challenges in Machine Learning"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_23"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00280"},{"key":"ref26","article-title":"Decoupled weight decay regularization","author":"loshchilov","year":"2019","journal-title":"ICLRE"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01267-0_44"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00495"},{"key":"ref11","article-title":"Explaining and harnessing adversarial examples","author":"goodfellow","year":"2015","journal-title":"ICLRE"},{"key":"ref40","article-title":"Examining the impact of blur on recognition by convolutional networks","author":"vasiljevic","year":"2016","journal-title":"CoRR"},{"key":"ref12","article-title":"Countering adversarial images using input transformations","author":"guo","year":"2018","journal-title":"ICLRE"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref14","article-title":"Benchmarking neural network robustness to common corruptions and perturbations","author":"hendrycks","year":"2019","journal-title":"ICLRE"},{"key":"ref15","article-title":"Augmix: A simple data processing method to improve robustness and uncertainty","author":"hendrycks","year":"2020","journal-title":"ICLRE"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref17","article-title":"Binarized neural networks","author":"hubara","year":"2016","journal-title":"NIPS"},{"key":"ref18","article-title":"Binaryduo: Reducing gradient mismatch in binary activation network by coupling binary activations","author":"kim","year":"2020","journal-title":"ICLRE"},{"key":"ref19","article-title":"Learning multiple layers of features from tiny images","author":"krizhevsky","year":"2009","journal-title":"Tech Rep"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.49"},{"key":"ref3","article-title":"Thermometer encoding: One hot way to resist adversarial examples","author":"buckman","year":"2018","journal-title":"ICLRE"},{"key":"ref6","article-title":"Binaryconnect: Training deep neural networks with binary weights during propagations","author":"courbariaux","year":"2015","journal-title":"NIPS"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/EuroSP.2019.00042"},{"key":"ref8","article-title":"Stochastic activation pruning for robust adversarial defense","author":"dhillon","year":"2018","journal-title":"ICLRE"},{"key":"ref7","article-title":"BNN+: improved binary network training","author":"darabi","year":"2018","journal-title":"CoRR"},{"key":"ref49","article-title":"Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients","author":"zhou","year":"2016","journal-title":"CoRR"},{"key":"ref9","article-title":"Generalisation in humans and deep neural networks","author":"geirhos","year":"2018","journal-title":"NeurIPS"},{"key":"ref46","article-title":"Lq-nets: Learned quantization for highly accurate and compact deep neural networks","author":"zhang","year":"2018","journal-title":"ECCV"},{"key":"ref45","article-title":"You only propagate once: Accelerating adversarial training via maximal principle","author":"zhang","year":"2019","journal-title":"NeurIPS"},{"key":"ref48","article-title":"Defending against whitebox adversarial attacks via randomized discretization","author":"zhang","year":"2019","journal-title":"PMLR"},{"key":"ref47","article-title":"Theoretically principled trade-off between robustness and accuracy","author":"zhang","year":"2019","journal-title":"ICML"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.634"},{"key":"ref41","article-title":"Mitigating adversarial effects through randomization","author":"xie","year":"2018","journal-title":"ICLRE"},{"key":"ref44","article-title":"Wide residual networks","author":"zagoruyko","year":"2016","journal-title":"BMVC"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2018.23198"}],"event":{"name":"2022 26th International Conference on Pattern Recognition (ICPR)","start":{"date-parts":[[2022,8,21]]},"location":"Montreal, QC, Canada","end":{"date-parts":[[2022,8,25]]}},"container-title":["2022 26th International Conference on Pattern Recognition (ICPR)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9956007\/9955631\/09956572.pdf?arnumber=9956572","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,19]],"date-time":"2022-12-19T20:05:45Z","timestamp":1671480345000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9956572\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,21]]},"references-count":49,"URL":"https:\/\/doi.org\/10.1109\/icpr56361.2022.9956572","relation":{},"subject":[],"published":{"date-parts":[[2022,8,21]]}}}