Training certifiably robust neural networks remains a notoriously hard problem. While adversarial training optimizes under-approximations of the worst-case loss, which leads to insufficient regularization for certification, sound certified training methods, optimize loose over-approximations, leading to over-regularization and poor (standard) accuracy. In this work, we propose TAPS, an (unsound) certified training method that combines IBP and PGD training to optimize more precise, although not necessarily sound, worst-case loss approximations, reducing over-regularization and increasing certified and standard accuracies. Empirically, TAPS achieves a new state-of-the-art in many settings, e.g., reaching a certified accuracy of 22% on TinyImageNet for L-inf-perturbations with radius eps=1/255.

@article{mao23taps author = {Yuhao Mao and Mark Niklas M{\"{u}}ller and Marc Fischer and Martin T. Vechev}, title = {{TAPS:} Connecting Certified and Adversarial Training}, year = {2023}, doi = {10.48550/arXiv.2305.04574}, eprinttype = {arXiv}, eprint = {2305.04574}}