Papers citing 'Robustness of classifiers: from adversarial to random noise'

Title
A Survey On Universal Adversarial AttackInternational Joint Conference on Artificial Intelligence (IJCAI), 2021 Chaoning Zhang Philipp Benz Chenguo Lin Adil Karjauv Jing Wu In So Kweon AAML 255 102 0 02 Mar 2021
Universal Adversarial Perturbations Through the Lens of Deep Steganography: Towards A Fourier PerspectiveAAAI Conference on Artificial Intelligence (AAAI), 2021 Chaoning Zhang Philipp Benz Adil Karjauv In So Kweon AAML 191 46 0 12 Feb 2021
Towards Optimal Branching of Linear and Semidefinite Relaxations for Neural Network Robustness Certification Brendon G. Anderson Ziye Ma Jingqi Li Somayeh Sojoudi 470 1 0 22 Jan 2021
Heating up decision boundaries: isocapacitory saturation, adversarial scenarios and generalization boundsInternational Conference on Learning Representations (ICLR), 2021 B. Georgiev L. Franken Mayukh Mukherjee AAML 104 2 0 15 Jan 2021
Unlearnable Examples: Making Personal Data UnexploitableInternational Conference on Learning Representations (ICLR), 2021 Hanxun Huang Jiabo He S. Erfani James Bailey Yisen Wang MIACV 458 230 0 13 Jan 2021
Characterizing the Evasion Attackability of Multi-label ClassifiersAAAI Conference on Artificial Intelligence (AAAI), 2020 Zhuo Yang Yufei Han Xiangliang Zhang AAML 150 11 0 17 Dec 2020
Achieving Adversarial Robustness Requires An Active TeacherJournal of Computational Mathematics (JCM), 2020 Chao Ma Lexing Ying 122 1 0 14 Dec 2020
Certifying Incremental Quadratic Constraints for Neural Networks via Convex OptimizationConference on Learning for Dynamics & Control (L4DC), 2020 Navid Hashemi Justin Ruths Mahyar Fazlyab 322 23 0 10 Dec 2020
SurFree: a fast surrogate-free black-box attackComputer Vision and Pattern Recognition (CVPR), 2020 Thibault Maho Teddy Furon Erwan Le Merrer AAML 157 108 0 25 Nov 2020
Sparse PCA: Algorithms, Adversarial Perturbations and CertificatesIEEE Annual Symposium on Foundations of Computer Science (FOCS), 2020 Tommaso dÓrsi Pravesh Kothari Gleb Novikov David Steurer AAML 247 27 0 12 Nov 2020
Optimism in the Face of Adversity: Understanding and Improving Deep Learning through Adversarial RobustnessProceedings of the IEEE (Proc. IEEE), 2020 Guillermo Ortiz-Jiménez Apostolos Modas Seyed-Mohsen Moosavi-Dezfooli P. Frossard AAML 316 50 0 19 Oct 2020
Certifying Neural Network Robustness to Random Input Noise from Samples Brendon G. Anderson Somayeh Sojoudi AAML 122 9 0 15 Oct 2020
Data-Driven Certification of Neural Networks with Random Input NoiseIEEE Transactions on Control of Network Systems (TCNS), 2020 Brendon G. Anderson Somayeh Sojoudi AAML 319 12 0 02 Oct 2020
Achieving Adversarial Robustness via SparsityMachine-mediated learning (ML), 2020 Shu-Fan Wang Ningyi Liao Liyao Xiang Nanyang Ye Quanshi Zhang AAML 155 18 0 11 Sep 2020
BREEDS: Benchmarks for Subpopulation ShiftInternational Conference on Learning Representations (ICLR), 2020 Shibani Santurkar Dimitris Tsipras Aleksander Madry OOD 187 189 0 11 Aug 2020
Adversarial Examples on Object Recognition: A Comprehensive SurveyACM Computing Surveys (ACM CSUR), 2020 A. Serban E. Poll Joost Visser AAML 361 78 0 07 Aug 2020
A General Framework For Detecting Anomalous Inputs to DNN ClassifiersInternational Conference on Machine Learning (ICML), 2020 Jayaram Raghuram Varun Chandrasekaran S. Jha Suman Banerjee AAML 223 38 0 29 Jul 2020
Learning from Noisy Labels with Deep Neural Networks: A SurveyIEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS), 2020 Hwanjun Song Minseok Kim Dongmin Park Yooju Shin Jae-Gil Lee NoLa 974 1,176 0 16 Jul 2020
Towards robust sensing for Autonomous Vehicles: An adversarial perspectiveIEEE Signal Processing Magazine (IEEE Signal Process. Mag.), 2020 Apostolos Modas Ricardo Sánchez-Matilla P. Frossard Andrea Cavallaro AAML 178 39 0 14 Jul 2020
Understanding Adversarial Examples from the Mutual Influence of Images and PerturbationsComputer Vision and Pattern Recognition (CVPR), 2020 Chaoning Zhang Philipp Benz Tooba Imtiaz In-So Kweon SSL AAML 170 134 0 13 Jul 2020
Towards an Adversarially Robust Normalization Approach Muhammad Awais Fahad Shamshad Sung-Ho Bae AAML OOD 190 21 0 19 Jun 2020
Lipschitz Bounds and Provably Robust Training by Laplacian Smoothing Vishaal Krishnan Abed AlRahman Al Makdah Fabio Pasqualetti OOD AAML 242 25 0 05 Jun 2020
Enhancing Resilience of Deep Learning Networks by Means of Transferable AdversariesIEEE International Joint Conference on Neural Network (IJCNN), 2020 M. Seiler Heike Trautmann P. Kerschke AAML 80 0 0 27 May 2020
Arms Race in Adversarial Malware Detection: A Survey Deqiang Li Qianmu Li Yanfang Ye Shouhuai Xu AAML 226 55 0 24 May 2020
Increasing-Margin Adversarial (IMA) Training to Improve Adversarial Robustness of Neural Networks Linhai Ma Liang Liang AAML 750 21 0 19 May 2020
Adversarial Robustness Guarantees for Random Deep Neural NetworksInternational Conference on Machine Learning (ICML), 2020 Giacomo De Palma B. Kiani S. Lloyd AAML OOD 131 9 0 13 Apr 2020
GeoDA: a geometric framework for black-box adversarial attacksComputer Vision and Pattern Recognition (CVPR), 2020 A. Rahmati Seyed-Mohsen Moosavi-Dezfooli P. Frossard H. Dai MLAU AAML 209 129 0 13 Mar 2020
Analyzing Accuracy Loss in Randomized Smoothing Defenses Yue Gao Harrison Rosenberg Kassem Fawaz S. Jha Justin Hsu AAML 148 6 0 03 Mar 2020
Learn2Perturb: an End-to-end Feature Perturbation Learning to Improve Adversarial RobustnessComputer Vision and Pattern Recognition (CVPR), 2020 Ahmadreza Jeddi M. Shafiee Michelle Karg C. Scharfenberger A. Wong OOD AAML 313 72 0 02 Mar 2020
Randomization matters. How to defend against strong adversarial attacksInternational Conference on Machine Learning (ICML), 2020 Rafael Pinot Raphael Ettedgui Geovani Rizk Y. Chevaleyre Jamal Atif AAML 233 62 0 26 Feb 2020
Mitigating Class Boundary Label Uncertainty to Reduce Both Model Bias and VarianceACM Transactions on Knowledge Discovery from Data (TKDD), 2020 Matthew Almeida Wei Ding S. Crouter Ping Chen 99 13 0 23 Feb 2020
Boosting Adversarial Training with Hypersphere EmbeddingNeural Information Processing Systems (NeurIPS), 2020 Tianyu Pang Xiao Yang Yinpeng Dong Kun Xu Jun Zhu Hang Su AAML 304 161 0 20 Feb 2020
Hold me tight! Influence of discriminative features on deep network boundariesNeural Information Processing Systems (NeurIPS), 2020 Guillermo Ortiz-Jiménez Apostolos Modas Seyed-Mohsen Moosavi-Dezfooli P. Frossard AAML 189 51 0 15 Feb 2020
A simple way to make neural networks robust against diverse image corruptions E. Rusak Lukas Schott Roland S. Zimmermann Julian Bitterwolf Oliver Bringmann Matthias Bethge Wieland Brendel 243 65 0 16 Jan 2020
Design of optical neural networks with component imprecisionsOptics Express (OE), 2019 Michael Y.-S. Fang S. Manipatruni Casimir Wierzynski A. Khosrowshahi M. DeWeese 147 141 0 13 Dec 2019
Walking on the Edge: Fast, Low-Distortion Adversarial ExamplesIEEE Transactions on Information Forensics and Security (IEEE TIFS), 2019 Hanwei Zhang Yannis Avrithis Teddy Furon Laurent Amsaleg AAML 160 53 0 04 Dec 2019
Loss Aware Post-training QuantizationMachine-mediated learning (ML), 2019 Yury Nahshan Brian Chmiel Chaim Baskin Evgenii Zheltonozhskii Ron Banner A. Bronstein A. Mendelson MQ 301 184 0 17 Nov 2019
Learning To Characterize Adversarial SubspacesIEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2019 Xiaofeng Mao YueFeng Chen Yuhong Li Yuan He Hui Xue AAML 124 13 0 15 Nov 2019
On Model Robustness Against Adversarial Examples Shufei Zhang Kaizhu Huang Zenglin Xu AAML 182 0 0 15 Nov 2019
On Robustness to Adversarial Examples and Polynomial OptimizationNeural Information Processing Systems (NeurIPS), 2019 Pranjal Awasthi Abhratanu Dutta Aravindan Vijayaraghavan OOD AAML 148 34 0 12 Nov 2019
Directional Adversarial Training for Cost Sensitive Deep Learning Classification ApplicationsEngineering applications of artificial intelligence (EAAI), 2019 M. Terzi Gian Antonio Susto Pratik Chaudhari OOD AAML 122 17 0 08 Oct 2019
Yet another but more efficient black-box adversarial attack: tiling and evolution strategies Laurent Meunier Cen Chen Li Wang MLAU AAML 210 42 0 05 Oct 2019
Lower Bounds on Adversarial Robustness from Optimal TransportNeural Information Processing Systems (NeurIPS), 2019 A. Bhagoji Daniel Cullina Prateek Mittal OOD OT AAML 160 97 0 26 Sep 2019
Mixup Inference: Better Exploiting Mixup to Defend Adversarial AttacksInternational Conference on Learning Representations (ICLR), 2019 Tianyu Pang Kun Xu Jun Zhu AAML 183 111 0 25 Sep 2019
Adversarial Attacks and Defenses in Images, Graphs and Text: A ReviewInternational Journal of Automation and Computing (IJAC), 2019 Han Xu Yao Ma Haochen Liu Debayan Deb Hui Liu Shucheng Zhou Anil K. Jain AAML 271 725 0 17 Sep 2019
On the Hardness of Robust Classification Pascale Gourdeau Varun Kanade Marta Z. Kwiatkowska J. Worrell 136 44 0 12 Sep 2019
PDA: Progressive Data Augmentation for General Robustness of Deep Neural Networks Hang Yu Aishan Liu Xianglong Liu Gen Li Ping Luo R. Cheng Jichen Yang Chongzhi Zhang AAML 147 12 0 11 Sep 2019
Are Adversarial Robustness and Common Perturbation Robustness Independent Attributes ? Alfred Laugros A. Caplier Matthieu Ospici AAML 140 44 0 04 Sep 2019
Automated Corrosion Detection Using Crowd Sourced Training for Deep Learning W. Nash Courtney Powell Tom Drummond N. Birbilis 55 33 0 04 Aug 2019
Adversarial Robustness via Label-Smoothing Morgane Goibert Elvis Dohmatob AAML 199 18 0 27 Jun 2019