Analysis of robust neural networks for control - Newton, M (2023)
The prevalence of neural networks in many application areas is expanding at an increasing rate, with the potential to provide huge benefits across numerous sectors. However, one of the greatest shortcomings of a trained neural network is its sensitivity to adversarial attacks. It is becoming clear that providing robust guarantees on systems that use neural networks is very important, especially in safety-critical applications. However, quantifying their safety and robustness properties has proven challenging due to the non-linearities of the activation functions inside the neural network.