An Estimator for the Sensitivity to Perturbations of Deep Neural
  Networks
Naman Maheshwari Nicholas Malaya Scott Moe Jaydeep P. Kulkarni Sudhanva Gurumurthi
For Deep Neural Networks (DNNs) to become useful in safety-critical
applications, such as self-driving cars and disease diagnosis, they must be
stable to perturbations in input and model parameters. Characterizing the
sensitivity of a DNN to perturbations is necessary to determine minimal
bit-width precision that may be used to safely represent the network. However,
no general result exists that is capable of predicting the sensitivity of a
given DNN to round-off error, noise, or other perturbations in input. This
paper derives an estimator that can predict such quantities. The estimator is
derived via inequalities and matrix norms, and the resulting quantity is
roughly analogous to a condition number for the entire neural network. An
approximation of the estimator is tested on two Convolutional Neural Networks,
AlexNet and VGG-19, using the ImageNet dataset. For each of these networks, the
tightness of the estimator is explored via random perturbations and adversarial
attacks.