Closed max-andr closed 4 years ago
Name: Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks
Authors: Maksym Andriushchenko, Matthias Hein
Paper: https://arxiv.org/abs/1906.03526
Code: https://github.com/max-andr/provably-robust-boosting
Venue: NeurIPS 2019
Does the code implement the robust-ml API and include pre-trained models: yes
Dataset: MNIST, FMNIST
Threat model: Linf (ϵ=0.3), Linf (ϵ=0.1)
Natural accuracy: 97.32%, 85.85% clean accuracy
Claims: 87.54%, 76.83% provable accuracy
The pairs of numbers mentioned always correspond to MNIST / FMNIST models respectively.
Thanks.
Thank you for the submission! It's on the site now: https://www.robust-ml.org/defenses/
I reworded "provable accuracy" to "certified" for consistency with other items in the table.
Name: Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks
Authors: Maksym Andriushchenko, Matthias Hein
Paper: https://arxiv.org/abs/1906.03526
Code: https://github.com/max-andr/provably-robust-boosting
Venue: NeurIPS 2019
Does the code implement the robust-ml API and include pre-trained models: yes
Dataset: MNIST, FMNIST
Threat model: Linf (ϵ=0.3), Linf (ϵ=0.1)
Natural accuracy: 97.32%, 85.85% clean accuracy
Claims: 87.54%, 76.83% provable accuracy
The pairs of numbers mentioned always correspond to MNIST / FMNIST models respectively.
Thanks.