Addressing Neural Network Robustness with Mixup and Targeted Labeling Adversarial Training
Authors: Alfred Laugros, Alice Caplier, Matthieu Ospici | Published: 2020-08-19
堅牢性向上手法
Provably robust deep generative models
Authors: Filipe Condessa, Zico Kolter | Published: 2020-04-22
Certifying Joint Adversarial Robustness for Model Ensembles
Authors: Mainuddin Ahmad Jonas, David Evans | Published: 2020-04-21
Luring of transferable adversarial perturbations in the black-box paradigm
Authors: Rémi Bernhard, Pierre-Alain Moellic, Jean-Max Dutertre | Published: 2020-04-10 | Updated: 2021-03-03
Adversarial Robustness for Code
Authors: Pavol Bielik, Martin Vechev | Published: 2020-02-11 | Updated: 2020-08-15
Robustness of Bayesian Neural Networks to Gradient-Based Attacks
Authors: Ginevra Carbone, Matthew Wicker, Luca Laurenti, Andrea Patane, Luca Bortolussi, Guido Sanguinetti | Published: 2020-02-11 | Updated: 2020-06-24
Improving the affordability of robustness training for DNNs
Authors: Sidharth Gupta, Parijat Dube, Ashish Verma | Published: 2020-02-11 | Updated: 2020-04-30
Fine-grained Uncertainty Modeling in Neural Networks
Authors: Rahul Soni, Naresh Shah, Jimmy D. Moore | Published: 2020-02-11
Testing Robustness Against Unforeseen Adversaries
Authors: Max Kaufmann, Daniel Kang, Yi Sun, Steven Basart, Xuwang Yin, Mantas Mazeika, Akul Arora, Adam Dziedzic, Franziska Boenisch, Tom Brown, Jacob Steinhardt, Dan Hendrycks | Published: 2019-08-21 | Updated: 2023-10-30
Denoising and Verification Cross-Layer Ensemble Against Black-box Adversarial Attacks
Authors: Ka-Ho Chow, Wenqi Wei, Yanzhao Wu, Ling Liu | Published: 2019-08-21 | Updated: 2019-10-26