{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T14:10:31Z","timestamp":1730211031819,"version":"3.28.0"},"reference-count":26,"publisher":"IEEE","license":[{"start":{"date-parts":[[2023,1,25]],"date-time":"2023-01-25T00:00:00Z","timestamp":1674604800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,1,25]],"date-time":"2023-01-25T00:00:00Z","timestamp":1674604800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,1,25]]},"DOI":"10.1109\/csicc58665.2023.10105351","type":"proceedings-article","created":{"date-parts":[[2023,4,26]],"date-time":"2023-04-26T17:57:09Z","timestamp":1682531829000},"page":"1-6","source":"Crossref","is-referenced-by-count":0,"title":["A Data-Centric Approach for Improving Adversarial Training Through the Lens of Out-of-Distribution Detection"],"prefix":"10.1109","author":[{"given":"Mohammad","family":"Azizmalayeri","sequence":"first","affiliation":[{"name":"Sharif University of Technology,Computer Engineering Department,Tehran,Iran"}]},{"given":"Arman","family":"Zarei","sequence":"additional","affiliation":[{"name":"Sharif University of Technology,Computer Engineering Department,Tehran,Iran"}]},{"given":"Alireza","family":"Isavand","sequence":"additional","affiliation":[{"name":"Sharif University of Technology,Computer Engineering Department,Tehran,Iran"}]},{"given":"Mohammad Taghi","family":"Manzuri","sequence":"additional","affiliation":[{"name":"Sharif University of Technology,Computer Engineering Department,Tehran,Iran"}]},{"given":"Mohammad Hossein","family":"Rohban","sequence":"additional","affiliation":[{"name":"Sharif University of Technology,Computer Engineering Department,Tehran,Iran"}]}],"member":"263","reference":[{"journal-title":"Neural Information Processing Systems (NeurIPS)","article-title":"A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks","year":"2018","author":"lee","key":"ref13"},{"journal-title":"International Conference on Learning Representations (ICLR)","article-title":"A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks","year":"0","author":"hendrycks","key":"ref12"},{"journal-title":"ArXiv Preprint","article-title":"Lagrangian Objective Function Leads to Improved Unforeseen Attack Generalization in Adversarial Training","year":"2021","author":"azizmalayeri","key":"ref15"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1201\/9781351251389-8"},{"journal-title":"ArXiv Preprint","article-title":"Instance adaptive adversarial training: Improved accuracy tradeoffs in neural nets","year":"2019","author":"balaji","key":"ref11"},{"journal-title":"Proc of the International Conference on Machine Learning (ICML)","article-title":"Perceptual Adversarial Robustness: Defense Against Unseen Threat Models","year":"0","author":"laidlaw","key":"ref10"},{"journal-title":"BERT Pre-training of deep bidirectional transformers for language understanding","year":"2016","author":"devlin","key":"ref2"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/95"},{"journal-title":"International Conference on Learning Representations (ICLR)","article-title":"Bag of Tricks for Adversarial Training","year":"0","author":"pang","key":"ref16"},{"journal-title":"Proc of the International Conference on Machine Learning (ICML)","article-title":"Theoretically Principled Trade-off between Robustness and Accuracy","year":"0","author":"zhang","key":"ref19"},{"journal-title":"International Conference on Learning Representations (ICLR)","article-title":"Improving Adversarial Robustness Requires Revisiting Misclassified Examples","year":"0","author":"wang","key":"ref18"},{"journal-title":"CoRR abs\/2106 09022","article-title":"A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection","year":"2021","author":"ren","key":"ref24"},{"journal-title":"NIPS Workshop on Deep Learning and Unsupervised Feature Learning","article-title":"Reading Digits in Natural Images with Unsupervised Feature Learning","year":"0","author":"netzer","key":"ref23"},{"journal-title":"ArXiv Preprint","article-title":"Max-margin adversarial (mma) training: Direct input space margin maximization through adversarial training","year":"2018","author":"ding","key":"ref26"},{"journal-title":"PMLR","article-title":"On the Convergence and Robustness of Adversarial Training","year":"2019","author":"wang","key":"ref25"},{"journal-title":"Proc of the International Conference on Machine Learning (ICML)","article-title":"Overfitting in adversarially robust deep learning","year":"0","author":"rice","key":"ref20"},{"journal-title":"Learning multiple layers of features from tiny images","year":"2009","author":"krizhevsky","key":"ref22"},{"journal-title":"CoRR abs\/2102 03482","article-title":"Understanding the Interaction of Adversarial Training with Noisy Labels","year":"2021","author":"zhu","key":"ref21"},{"journal-title":"International Conference on Learning Representations (ICLR)","article-title":"Towards deep learning models resistant to adversarial attacks","year":"0","author":"madry","key":"ref8"},{"journal-title":"Proc of the International Conference on Machine Learning (ICML)","article-title":"Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples","year":"0","author":"athalye","key":"ref7"},{"journal-title":"Neural Information Processing Systems (NeurIPS)","article-title":"Data Augmentation Can Improve Robustness","year":"2021","author":"rebuffi","key":"ref9"},{"journal-title":"International Conference on Learning Representations (ICLR)","article-title":"Explaining and harnessing adversarial examples","year":"0","author":"goodfellow","key":"ref4"},{"journal-title":"ArXiv Preprint","article-title":"Intriguing properties of neural networks","year":"2013","author":"szegedy","key":"ref3"},{"journal-title":"Computer Vision and Pattern Recognition (CVPR)","article-title":"End to end learning for self-driving cars","year":"2018","author":"bojarski","key":"ref6"},{"key":"ref5","article-title":"PIAT: Physics Informed Adversarial Training for Solving Partial Differential Equations","volume":"6647","author":"shekarpaz","year":"2022","journal-title":"ArXiv Preprint"}],"event":{"name":"2023 28th International Computer Conference, Computer Society of Iran (CSICC)","start":{"date-parts":[[2023,1,25]]},"location":"Tehran, Iran, Islamic Republic of","end":{"date-parts":[[2023,1,26]]}},"container-title":["2023 28th International Computer Conference, Computer Society of Iran (CSICC)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10105300\/10105309\/10105351.pdf?arnumber=10105351","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,15]],"date-time":"2023-05-15T17:45:38Z","timestamp":1684172738000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10105351\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,25]]},"references-count":26,"URL":"https:\/\/doi.org\/10.1109\/csicc58665.2023.10105351","relation":{},"subject":[],"published":{"date-parts":[[2023,1,25]]}}}