Improving Machine Learning Robustness via Adversarial Training

Improving Machine Learning Robustness via Adversarial Training

22 September 2023

T. Hapuarachchi

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Papers citing "Improving Machine Learning Robustness via Adversarial Training"

18 / 18 papers shown

Title
ML Attack Models: Adversarial Attacks and Data Poisoning Attacks Jing Lin Long Dang Mohamed Rahouti Kaiqi Xiong AAML 60 48 0 06 Dec 2021
Adversarial training in communication constrained federated learning Devansh Shah Parijat Dube Supriyo Chakraborty Ashish Verma FedML 79 34 0 01 Mar 2021
FAT: Federated Adversarial Training Giulio Zizzo Ambrish Rawat M. Sinn Beat Buesser FedML 56 43 0 03 Dec 2020
An Adversarial Attack Defending System for Securing In-Vehicle Networks Yi Li Jing Lin Kaiqi Xiong AAML 80 16 0 25 Aug 2020
Overcoming Forgetting in Federated Learning on Non-IID Data N. Shoham Tomer Avidor Aviv Keren Nadav Tal-Israel Daniel Benditkis Liron Mor Yosef Itai Zeitak CLL FedML 130 226 0 17 Oct 2019
When Does Label Smoothing Help? Rafael Müller Simon Kornblith Geoffrey E. Hinton UQCV 207 1,955 0 06 Jun 2019
Federated Machine Learning: Concept and Applications Qiang Yang Yang Liu Tianjian Chen Yongxin Tong FedML 81 2,332 0 13 Feb 2019
Adversarial Robustness Toolbox v1.0.0 Maria-Irina Nicolae M. Sinn Minh-Ngoc Tran Beat Buesser Ambrish Rawat ... Nathalie Baracaldo Bryant Chen Heiko Ludwig Ian Molloy Ben Edwards AAML VLM 77 460 0 03 Jul 2018
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms Han Xiao Kashif Rasul Roland Vollgraf 285 8,926 0 25 Aug 2017
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 317 12,138 0 19 Jun 2017
Towards Evaluating the Robustness of Neural Networks Nicholas Carlini D. Wagner OOD AAML 282 8,587 0 16 Aug 2016
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 547 5,912 0 08 Jul 2016
Communication-Efficient Learning of Deep Networks from Decentralized Data H. B. McMahan Eider Moore Daniel Ramage S. Hampson Blaise Agüera y Arcas FedML 408 17,615 0 17 Feb 2016
Rethinking the Inception Architecture for Computer Vision Christian Szegedy Vincent Vanhoucke Sergey Ioffe Jonathon Shlens Z. Wojna 3DV BDL 886 27,427 0 02 Dec 2015
DeepFool: a simple and accurate method to fool deep neural networks Seyed-Mohsen Moosavi-Dezfooli Alhussein Fawzi P. Frossard AAML 154 4,905 0 14 Nov 2015
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 282 19,129 0 20 Dec 2014
ImageNet Large Scale Visual Recognition Challenge Olga Russakovsky Jia Deng Hao Su J. Krause S. Satheesh ... A. Karpathy A. Khosla Michael S. Bernstein Alexander C. Berg Li Fei-Fei VLM ObjD 1.7K 39,615 0 01 Sep 2014
Intriguing properties of neural networks Christian Szegedy Wojciech Zaremba Ilya Sutskever Joan Bruna D. Erhan Ian Goodfellow Rob Fergus AAML 284 14,968 1 21 Dec 2013