  Deep neural networks (DNNs) exhibit superior performance in various machine
learning tasks, e.g., image classification, speech recognition, biometric
recognition, object detection, etc. However, it is essential to analyze their
sensitivity to parameter perturbations before deploying them in real-world
applications. In this work, we assess the sensitivity of DNNs against
perturbations to their weight and bias parameters. The sensitivity analysis
involves three DNN architectures (VGG, ResNet, and DenseNet), three types of
parameter perturbations (Gaussian noise, weight zeroing, and weight scaling),
and two settings (entire network and layer-wise). We perform experiments in the
context of iris presentation attack detection and evaluate on two publicly
available datasets: LivDet-Iris-2017 and LivDet-Iris-2020. Based on the
sensitivity analysis, we propose improved models simply by perturbing
parameters of the network without undergoing training. We further combine these
perturbed models at the score-level and at the parameter-level to improve the
performance over the original model. The ensemble at the parameter-level shows
an average improvement of 43.58% on the LivDet-Iris-2017 dataset and 9.25% on
the LivDet-Iris-2020 dataset. The source code is available at
https://github.com/redwankarimsony/WeightPerturbation-MSU.
