  Deep Neural Networks (DNNs) are increasingly deployed in safety-critical
applications including autonomous vehicles and medical diagnostics. To reduce
the residual risk for unexpected DNN behaviour and provide evidence for their
trustworthy operation, DNNs should be thoroughly tested. The DeepFault whitebox
DNN testing approach presented in our paper addresses this challenge by
employing suspiciousness measures inspired by fault localization to establish
the hit spectrum of neurons and identify suspicious neurons whose weights have
not been calibrated correctly and thus are considered responsible for
inadequate DNN performance. DeepFault also uses a suspiciousness-guided
algorithm to synthesize new inputs, from correctly classified inputs, that
increase the activation values of suspicious neurons. Our empirical evaluation
on several DNN instances trained on MNIST and CIFAR-10 datasets shows that
DeepFault is effective in identifying suspicious neurons. Also, the inputs
synthesized by DeepFault closely resemble the original inputs, exercise the
identified suspicious neurons and are highly adversarial.
