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Generalized Universal Adversarial Perturbations

Paper analysis

Universal Adversarial Perturbations

Posted by Yanghao ZHANG on August 2, 2020

On Breaking Deep Generative Model-based Defenses and Beyond

Paper analysis

On Breaking Deep Generative Model-based Defenses and Beyond

Posted by Yanghao ZHANG on July 22, 2020

Attacks Which Do Not Kill Training Make Adversarial Learning Stronger

Paper analysis

Attacks Which Do Not Kill Training Make Adversarial Learning Stronger

Posted by Yanghao ZHANG on July 22, 2020

Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers

Paper analysis

Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers Microsoft: Hadi Salman, Greg Yang, Jerry Li, Pengchuan Zhang, Huan Zhang, Ilya Razenshteyn, Sébastien Bubeck Contribut...

Posted by Yanghao ZHANG on July 12, 2020

Certified Adversarial Robustness via Randomized Smoothing

Paper analysis

Certified Adversarial Robustness via Randomized Smoothing CMU: Jeremy Cohen, Elan Rosenfeld, J. Zico Kolter Prove a tight robustness guarantee in $l_2$ norm for s...

Posted by Yanghao ZHANG on July 11, 2020

Certified robustness to adversarial examples with differential privacy

Paper analysis

Certified robustness to adversarial examples with differential privacy Columbia University: Mathias Lecuyer, Vaggelis Atlidakis, Roxana Geambasu, Daniel Hsu, and Suman Jana Definition of differen...

Posted by Yanghao ZHANG on July 10, 2020

Paper of Randomized smoothing

Paper list

Paper list of Randomized Smoothinng Certified robustness to adversarial examples with differential privacy, 2018 Certified Adversarial Robustness with Additive Noise, 2019 ...

Posted by Yanghao ZHANG on July 8, 2020

ICML20 paper reading (continuous updates)

Virtual Conference

Overfitting in adversarially robust deep learning (CMU) Plenty experiments shows that overfitting to the training set does in fact harm robust performance to a very large degree in adversarially r...

Posted by Yanghao ZHANG on July 7, 2020