乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,3,27]],"date-time":"2024-03-27T00:43:25Z","timestamp":1711500205148},"reference-count":58,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"2","license":[{"start":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T00:00:00Z","timestamp":1709251200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T00:00:00Z","timestamp":1709251200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T00:00:00Z","timestamp":1709251200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2020YFB1406900"]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62006181","62161160337","62132011","U21B2018","U20A20177"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shaanxi Province Key Industry Innovation Program","award":["2021ZDLGY01-02"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Dependable and Secure Comput."],"published-print":{"date-parts":[[2024,3]]},"DOI":"10.1109\/tdsc.2022.3184594","type":"journal-article","created":{"date-parts":[[2022,6,21]],"date-time":"2022-06-21T19:41:29Z","timestamp":1655840489000},"page":"530-541","source":"Crossref","is-referenced-by-count":1,"title":["Towards Gradient-Based Saliency Consensus Training for Adversarial Robustness"],"prefix":"10.1109","volume":"21","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-1073-7810","authenticated-orcid":false,"given":"Qian","family":"Li","sequence":"first","affiliation":[{"name":"Ministry of Education Key Laboratory for Intelligent Networks and Network Security, School of Cyber Science and Engineering, Xi'an Jiaotong University, Xi'an, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-6959-0569","authenticated-orcid":false,"given":"Chao","family":"Shen","sequence":"additional","affiliation":[{"name":"Ministry of Education Key Laboratory for Intelligent Networks and Network Security, School of Cyber Science and Engineering, Xi'an Jiaotong University, Xi'an, China"}]},{"given":"Qingyuan","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-6265-7345","authenticated-orcid":false,"given":"Chenhao","family":"Lin","sequence":"additional","affiliation":[{"name":"Ministry of Education Key Laboratory for Intelligent Networks and Network Security, School of Cyber Science and Engineering, Xi'an Jiaotong University, Xi'an, China"}]},{"given":"Xiang","family":"Ji","sequence":"additional","affiliation":[{"name":"Ministry of Education Key Laboratory for Intelligent Networks and Network Security, School of Cyber Science and Engineering, Xi'an Jiaotong University, Xi'an, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-0394-4432","authenticated-orcid":false,"given":"Saiyu","family":"Qi","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, China"}]}],"member":"263","reference":[{"key":"ref1","article-title":"Intriguing properties of neural networks","volume-title":"Proc. 2nd Int. Conf. Learn. Representations","author":"Szegedy"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.49"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00059"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00488"},{"key":"ref5","article-title":"Defense-GAN: Protecting classifiers against adversarial attacks using generative models","volume-title":"Proc. 6th Int. Conf. Learn. Representations","author":"Samangouei"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00191"},{"key":"ref7","first-page":"7472","article-title":"Theoretically principled trade-off between robustness and accuracy","volume-title":"Proc. 36th Int. Conf. Mach. Learn.","author":"Zhang"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.06083"},{"key":"ref9","article-title":"MMA training: Direct input space margin maximization through adversarial training","volume-title":"Proc. 8th Int. Conf. Learn. Representations","author":"Ding"},{"key":"ref10","article-title":"Jacobian adversarially regularized networks for robustness","volume-title":"Proc. 8th Int. Conf. Learn. Representations","author":"Chan"},{"key":"ref11","article-title":"Robustness may be at odds with accuracy","volume-title":"Proc. 7th Int. Conf. Learn. Representations","author":"Tsipras"},{"key":"ref12","first-page":"1823","article-title":"On the connection between adversarial robustness and saliency map interpretability","volume-title":"Proc. 36th Int. Conf. Mach. Learn.","author":"Etmann"},{"key":"ref13","article-title":"Stochastic activation pruning for robust adversarial defense","volume-title":"Proc. 6th Int. Conf. Learn. Representations","author":"Dhillon"},{"key":"ref14","article-title":"Thermometer encoding: One hot way to resist adversarial examples","volume-title":"Proc. 6th Int. Conf. Learn. Representations","author":"Buckman"},{"key":"ref15","first-page":"854","article-title":"Parseval networks: Improving robustness to adversarial examples","volume-title":"Proc. 34th Int. Conf. Mach. Learn.","author":"Cisse"},{"key":"ref16","first-page":"7025","article-title":"ME-Net: Towards effective adversarial robustness with matrix estimation","volume-title":"Proc. 36th Int. Conf. Mach. Learn.","author":"Yang"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/324"},{"key":"ref18","article-title":"Fast is better than free: Revisiting adversarial training","volume-title":"Proc. 8th Int. Conf. Learn. Representations","author":"Wong"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2020.3047752"},{"key":"ref20","article-title":"Explaining and harnessing adversarial examples","volume-title":"Proc. 3rd Int. Conf. Learn. Representations","author":"Goodfellow"},{"key":"ref21","first-page":"274","article-title":"Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples","volume-title":"Proc. 35th Int. Conf. Mach. Learn.","author":"Athalye"},{"key":"ref22","first-page":"5858","article-title":"Adversarial training and robustness for multiple perturbations","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Tram\u00e8r"},{"key":"ref23","article-title":"Logit pairing methods can fool gradient-based attacks","author":"Mosbach","year":"2018"},{"key":"ref24","first-page":"13 847","article-title":"Adversarial robustness through local linearization","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Qin"},{"key":"ref25","article-title":"Towards the first adversarially robust neural network model on MNIST","volume-title":"Proc. 7th Int. Conf. Learn. Representations","author":"Schott"},{"key":"ref26","first-page":"1829","article-title":"Defense against adversarial attacks using feature scattering-based adversarial training","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Zhang"},{"key":"ref27","first-page":"3358","article-title":"Adversarial training for free!","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Shafahi"},{"key":"ref28","first-page":"23 258","article-title":"Probabilistic margins for instance reweighting in adversarial training","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Wang"},{"key":"ref29","first-page":"3564","article-title":"Maximum mean discrepancy test is aware of adversarial attacks","volume-title":"Proc. 38th Int. Conf. Mach. Learn.","author":"Gao"},{"key":"ref30","first-page":"3145","article-title":"Learning important features through propagating activation differences","volume-title":"Proc. 34th Int. Conf. Mach. Learn.","author":"Shrikumar"},{"key":"ref31","article-title":"SmoothGrad: Removing noise by adding noise","author":"Smilkov","year":"2017"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2018.00097"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-019-01228-7"},{"key":"ref34","first-page":"14 300","article-title":"Robust attribution regularization","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Chen"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N16-3020"},{"key":"ref36","first-page":"15 857","article-title":"On relating explanations and adversarial examples","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Ignatiev"},{"key":"ref37","first-page":"13 567","article-title":"Explanations can be manipulated and geometry is to blame","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Dombrowski"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33013681"},{"key":"ref39","first-page":"7502","article-title":"Interpreting adversarially trained convolutional neural networks","volume-title":"Proc. 36th Int. Conf. Mach. Learn.","author":"Zhang"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-67661-2_17"},{"key":"ref41","article-title":"Adversarially robust transfer learning","volume-title":"Proc. 8th Int. Conf. Learn. Representations","author":"Shafahi"},{"key":"ref42","first-page":"9525","article-title":"Sanity checks for saliency maps","volume-title":"Proc. 32nd Int. Conf. Neural Inf. Process. Syst.","author":"Adebayo"},{"key":"ref43","article-title":"Revisiting sanity checks for saliency maps","author":"Yona","year":"2021"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1811.12231"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.3390\/rs13040612"},{"key":"ref46","article-title":"Learn to pay attention","volume-title":"Proc. 6th Int. Conf. Learn. Representations","author":"Jetley"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"issue":"4","key":"ref48","article-title":"Learning multiple layers of features from tiny images","volume":"1","author":"Krizhevsky","year":"2009"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref50","article-title":"Adversarial examples in modern machine learning: A review","author":"Wiyatno","year":"2019"},{"key":"ref51","article-title":"Robust learning with jacobian regularization","author":"Hoffman","year":"2019"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2020.3042083"},{"key":"ref53","article-title":"Adversarial distributional training for robust deep learning","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Dong"},{"key":"ref54","article-title":"Foolbox: A python toolbox to benchmark the robustness of machine learning models","author":"Rauber","year":"2017"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11504"},{"key":"ref56","article-title":"Adversarial logit pairing","author":"Kannan","year":"2018"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.5244\/C.30.87"},{"key":"ref58","first-page":"8093","article-title":"Overfitting in adversarially robust deep learning","volume-title":"Proc. 37th Int. Conf. Mach. Learn.","author":"Rice"}],"container-title":["IEEE Transactions on Dependable and Secure Computing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8858\/10472294\/09802782.pdf?arnumber=9802782","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,26]],"date-time":"2024-03-26T11:42:01Z","timestamp":1711453321000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9802782\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3]]},"references-count":58,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.1109\/tdsc.2022.3184594","relation":{},"ISSN":["1545-5971","1941-0018","2160-9209"],"issn-type":[{"value":"1545-5971","type":"print"},{"value":"1941-0018","type":"electronic"},{"value":"2160-9209","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3]]}}}