Training Robust Neural Networks Using Lipschitz Bounds

• Published in: IEEE control systems letters Vol. 6; pp. 121 - 126
• Main Authors: Pauli, Patricia, Koch, Anne, Berberich, Julian, Kohler, Paul, Allgower, Frank
• Format: Journal Article
• Language: English
• Published: IEEE 2022
• Subjects: Artificial neural networks; Estimation; Linear matrix inequalities; neural networks; Neurons; Perturbation methods; Robustness; Training; Upper bound
• ISSN: 2475-1456; 2475-1456
• DOI: 10.1109/LCSYS.2021.3050444

Due to their susceptibility to adversarial perturbations, neural networks (NNs) are hardly used in safety-critical applications. One measure of robustness to such perturbations in the input is the Lipschitz constant of the input-output map defined by an NN. In this letter, we propose a framework to train multi-layer NNs while at the same time encouraging robustness by keeping their Lipschitz constant small, thus addressing the robustness issue. More specifically, we design an optimization scheme based on the Alternating Direction Method of Multipliers that minimizes not only the training loss of an NN but also its Lipschitz constant resulting in a semidefinite programming based training procedure that promotes robustness. We design two versions of this training procedure. The first one includes a regularizer that penalizes an accurate upper bound on the Lipschitz constant. The second one allows to enforce a desired Lipschitz bound on the NN at all times during training. Finally, we provide two examples to show that the proposed framework successfully increases the robustness of NNs.