Robust Models are less Over-Confident

Julia Grabinski, Paul Gavrikov, Janis Keuper, Margret Keuper

Presented at: Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS)

Paper

Abstract: We empirically show that adversarially robust models are less over-confident then their non-robust counterparts. Abstract: Regardless of the success of convolutional neural networks (CNNs) in many academic benchmarks of computer vision tasks, their application in real-world is still facing fundamental challenges, like the inherent lack of robustness as unveiled by adversarial attacks. These attacks target to manipulate the network's prediction by adding a small amount of noise onto the input. In turn, adversarial training (AT) aims to achieve robustness against such attacks by including adversarial samples in the trainingset. However, a general analysis of the reliability and model calibration of these robust models beyond adversarial robustness is still pending. In this paper, we analyze a variety of adversarially trained models that achieve high robust accuracies when facing state-of-the-art attacks and we show that AT has an interesting side-effect: it leads to models that are significantly less overconfident with their decisions even on clean data than non-robust models. Further, our analysis of robust models shows that not only AT but also the model's building blocks (activation functions and pooling) have a strong influence on the models' confidence.

Model Zoo

Install it by:

pip install git+https://github.com/RobustBench/robustbench.git@v1.0
pip install robustvsnormalzoo

and then obtain pretrained models as simple as:

import robustvsnormalzoo

model = robustvsnormalzoo.load_model(dataset, paper_id, robust)

Set robust=True to download the robust checkpoint, and False for the normal one.

Supported combinations can be queried by:

robustvsnormalzoo.list_models()

Details

dataset	paper_id
cifar10	Andriushchenko2020Understanding
cifar10	Carmon2019Unlabeled
cifar10	Sehwag2020Hydra
cifar10	Wang2020Improving
cifar10	Hendrycks2019Using
cifar10	Rice2020Overfitting
cifar10	Zhang2019Theoretically
cifar10	Engstrom2019Robustness
cifar10	Chen2020Adversarial
cifar10	Huang2020Self
cifar10	Pang2020Boosting
cifar10	Wong2020Fast
cifar10	Ding2020MMA
cifar10	Zhang2019You
cifar10	Zhang2020Attacks
cifar10	Wu2020Adversarial_extra
cifar10	Wu2020Adversarial
cifar10	Gowal2020Uncovering_70_16
cifar10	Gowal2020Uncovering_70_16_extra
cifar10	Gowal2020Uncovering_34_20
cifar10	Gowal2020Uncovering_28_10_extra
cifar10	Sehwag2021Proxy
cifar10	Sehwag2021Proxy_R18
cifar10	Sitawarin2020Improving
cifar10	Chen2020Efficient
cifar10	Cui2020Learnable_34_20
cifar10	Cui2020Learnable_34_10
cifar10	Zhang2020Geometry
cifar10	Rebuffi2021Fixing_28_10_cutmix_ddpm
cifar10	Rebuffi2021Fixing_106_16_cutmix_ddpm
cifar10	Rebuffi2021Fixing_70_16_cutmix_ddpm
cifar10	Rebuffi2021Fixing_70_16_cutmix_extra
cifar10	Sridhar2021Robust
cifar10	Sridhar2021Robust_34_15
cifar10	Rebuffi2021Fixing_R18_ddpm
cifar10	Rade2021Helper_R18_extra
cifar10	Rade2021Helper_R18_ddpm
cifar10	Rade2021Helper_extra
cifar10	Rade2021Helper_ddpm
cifar10	Huang2021Exploring
cifar10	Huang2021Exploring_ema
cifar10	Addepalli2021Towards_RN18
cifar10	Addepalli2021Towards_WRN34
cifar10	Gowal2021Improving_70_16_ddpm_100m
cifar10	Dai2021Parameterizing
cifar10	Gowal2021Improving_28_10_ddpm_100m
cifar10	Gowal2021Improving_R18_ddpm_100m
cifar10	Chen2021LTD_WRN34_10
cifar10	Chen2021LTD_WRN34_20
cifar100	Gowal2020Uncovering
cifar100	Gowal2020Uncovering_extra
cifar100	Cui2020Learnable_34_20_LBGAT6
cifar100	Cui2020Learnable_34_10_LBGAT0
cifar100	Cui2020Learnable_34_10_LBGAT6
cifar100	Chen2020Efficient
cifar100	Wu2020Adversarial
cifar100	Sitawarin2020Improving
cifar100	Hendrycks2019Using
cifar100	Rice2020Overfitting
cifar100	Rebuffi2021Fixing_70_16_cutmix_ddpm
cifar100	Rebuffi2021Fixing_28_10_cutmix_ddpm
cifar100	Rebuffi2021Fixing_R18_ddpm
cifar100	Rade2021Helper_R18_ddpm
cifar100	Addepalli2021Towards_PARN18
cifar100	Addepalli2021Towards_WRN34
cifar100	Chen2021LTD_WRN34_10
imagenet	Wong2020Fast
imagenet	Engstrom2019Robustness
imagenet	Salman2020Do_R50
imagenet	Salman2020Do_R18
imagenet	Salman2020Do_50_2

Citation

Would you like to reference our evaluation?
Then consider citing our paper:

@inproceedings{
  grabinski2022robust,
  title={Robust Models are less Over-Confident},
  author={Julia Grabinski and Paul Gavrikov and Janis Keuper and Margret Keuper},
  booktitle={Advances in Neural Information Processing Systems},
  editor={Alice H. Oh and Alekh Agarwal and Danielle Belgrave and Kyunghyun Cho},
  year={2022},
  url={https://openreview.net/forum?id=5K3uopkizS}
}

Legal

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. Please note that robust and normal imagenet models are not provided by us and are licensed differently.