Reachable Sets of Classifiers and Regression Models: (Non-)Robustness Analysis and Robust Training
This work addresses robustness and interpretability issues in neural networks for both classification and regression tasks, offering a versatile tool for practitioners, though it builds incrementally on existing reachable set concepts.
The authors tackled the challenge of understanding neural network behavior by computing reachable sets to analyze robustness, explainability, and reliability. They developed efficient methods for over- and under-approximations, outperforming adversarial attacks and state-of-the-art verification methods for non-norm bound perturbations.
Neural networks achieve outstanding accuracy in classification and regression tasks. However, understanding their behavior still remains an open challenge that requires questions to be addressed on the robustness, explainability and reliability of predictions. We answer these questions by computing reachable sets of neural networks, i.e. sets of outputs resulting from continuous sets of inputs. We provide two efficient approaches that lead to over- and under-approximations of the reachable set. This principle is highly versatile, as we show. First, we use it to analyze and enhance the robustness properties of both classifiers and regression models. This is in contrast to existing works, which are mainly focused on classification. Specifically, we verify (non-)robustness, propose a robust training procedure, and show that our approach outperforms adversarial attacks as well as state-of-the-art methods of verifying classifiers for non-norm bound perturbations. Second, we provide techniques to distinguish between reliable and non-reliable predictions for unlabeled inputs, to quantify the influence of each feature on a prediction, and compute a feature ranking.