🚀 LiResNet: An Network Architecture for Training Certifiable Robust Models

This repository provides the implementation of our cutting-edge research on certifiable robust models. We proudly present the LiResNet model introduced at NeurIPS 2023 and its subsequent improvements now available as a preprint on arXiv. Our works are based on the training and certification schemes in GloRo Nets.

• [NeurIPS 2023] Unlocking Deterministic Robustness Certification on ImageNet
• [ICLR 2024] Effectively Leveraging Capacity for Improved Deterministic Robustness Certification

🚀 Getting Started:

• For training a model, check out our run.sh as a starting point.
• Dive into our configs for additional dataset configurations.

🔜 Upcoming Updates:

• A comprehensive README.
• Pre-trained model checkpoints.

📈 Main Results:

dataset	clean accuracy	VRA@36/255	VRA@72/255	VRA@108/255
CIFAR-10	87.0%	78.1%	66.6%	53.5%
CIFAR-100	62.1%	50.1%	38.5%	29.0%
Tiny-ImageNet	48.4%	37.0%	26.8%	18.6%
ImageNet	49.0%	38.3%	-	-

🤝 Support:

• Encountering issues? Submit an Issue.
• For specific inquiries, 📧 drop an email to kaihu@cmu.edu.

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License

This project is licensed under the MIT License - see the LICENSE file for details.

Citations

If you find this repository useful, consider to use the following citations

@INPROCEEDINGS{hu2023scaling,
    title={Unlocking Deterministic Robustness Certification on ImageNet},
    author={Kai Hu and Andy Zou and Zifan Wang and Klas Leino and Matt Fredrikson},
    booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
    year={2023},
    url={https://openreview.net/forum?id=SHyVaWGTO4}
}

@misc{hu2023recipe,
    title={A Recipe for Improved Certifiable Robustness: Capacity and Data}, 
    author={Kai Hu and Klas Leino and Zifan Wang and Matt Fredrikson},
    year={2023},
    eprint={2310.02513},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

@INPROCEEDINGS{leino21gloro,
    title = {Globally-Robust Neural Networks},
    author = {Klas Leino and Zifan Wang and Matt Fredrikson},
    booktitle = {International Conference on Machine Learning (ICML)},
    year = {2021}
}