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The growing use of deep neural networks (DNNs) in safety- andsecurity-critical areas like autonomous driving raises the need for theirsystematic testing. Coverage-guided testing (CGT) is an approach that appliesmutation or fuzzing according to a predefined coverage metric to find inputsthat cause misbehavior. With the introduction of a neuron coverage metric, CGThas also recently been applied to DNNs. In this work, we apply CGT to the taskof person detection in crowded scenes. The proposed pipeline uses YOLOv3 forperson detection and includes finding DNN bugs via sampling and mutation, andsubsequent DNN retraining on the updated training set. To be a bug, we requirea mutated image to cause a significant performance drop compared to a cleaninput. In accordance with the CGT, we also consider an additional requirementof increased coverage in the bug definition. In order to explore several typesof robustness, our approach includes natural image transformations,corruptions, and adversarial examples generated with the Daedalus attack. Theproposed framework has uncovered several thousand cases of incorrect DNNbehavior. The relative change in mAP performance of the retrained modelsreached on average between 26.21\% and 64.24\% for different robustness types.However, we have found no evidence that the investigated coverage metrics canbe advantageously used to improve robustness.

Is Neuron Coverage Needed to Make Person Detection More Robust?