Automated Feature Extraction for Testing Deep Learning Systems Through Illumination Search

The opacity of deep neural networks (DNNs) poses challenges in understanding the causes of their misbehaviours. Illumination search characterizes the inputs of a DNN by means of relevant features and explores the resulting feature map extensively. This facilitates the interpretation of misbehaviour-inducing inputs based on the regions they occupy in the feature map. However, current illumination-based approaches necessitate human expert involvement for the definition of the features, limiting broad applicability. In this paper, we address these limitations with DeepTheia, our fully automated illumination-based test generator that automatically extracts the features and explores the feature space using cutting-edge diffusion models. Experimental results show that DeepTheia consistently extracts highly discriminative features. Independent human assessors certified that DeepTheia is able to group misbehaviour-inducing inputs in a way that is understandable to humans in over 78% of the cases. Moreover, the inputs generated by DeepTheia were useful in significantly improving the ability of the original DL systems to handle inputs with critical feature combinations through fine-tuning.