Title: TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing,
Abstract: Machine learning models are notoriously difficult to interpret and debug.
This is particularly true of neural networks. In this work, we introduce
automated software testing techniques for neural networks that are well-suited
to discovering errors which occur only for rare inputs. Specifically, we
develop coverage-guided fuzzing (CGF) methods for neural networks. In CGF,
random mutations of inputs to a neural network are guided by a coverage metric
toward the goal of satisfying user-specified constraints. We describe how fast
approximate nearest neighbor algorithms can provide this coverage metric. We
then discuss the application of CGF to the following goals: finding numerical
errors in trained neural networks, generating disagreements between neural
networks and quantized versions of those networks, and surfacing undesirable
behavior in character level language models. Finally, we release an open source
library called TensorFuzz that implements the described techniques.