Enhancing Certified Robustness via Block Reflector Orthgonal Layers & Logit Anealing Loss

What is certified robustness?

Unlike empirical robustness, which focuses on improving model performance on adversarial examples, certified robustness provides provable guarantees that a model will maintain its performance within a specified perturbation radius. This means that even under adversarial attacks, the model's predictions remain stable and reliable.

Our Contributions

We introduce two core innovations to build more effective certified-robust networks:

• Block Reflector Orthogonal Layer (BRO): A low-rank, approximation-free orthogonal convolutional layer designed to efficiently construct Lipschitz neural networks, improving both stability and expressiveness.
• Logit Annealing Loss (LA): An adaptive loss function that anneals margin allocation across training samples, dynamically balancing classification margins to improve certified robustness

Citation

@inproceedings{lai2025enhancing,
    title={Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss},
    author={Bo-Han Lai and Pin-Han Huang and Bo-Han Kung and Shang-Tse Chen},
    booktitle={International Conference on Machine Learning (ICML)},
    year={2025},
    note={Spotlight}
}