  Bayesian Neural Networks (BNNs), unlike Traditional Neural Networks (TNNs)
are robust and adept at handling adversarial attacks by incorporating
randomness. This randomness improves the estimation of uncertainty, a feature
lacking in TNNs. Thus, we investigate the robustness of BNNs to white-box
attacks using multiple Bayesian neural architectures. Furthermore, we create
our BNN model, called BNN-DenseNet, by fusing Bayesian inference (i.e.,
variational Bayes) to the DenseNet architecture, and BDAV, by combining this
intervention with adversarial training. Experiments are conducted on the
CIFAR-10 and FGVC-Aircraft datasets. We attack our models with strong white-box
attacks ($l_\infty$-FGSM, $l_\infty$-PGD, $l_2$-PGD, EOT $l_\infty$-FGSM, and
EOT $l_\infty$-PGD). In all experiments, at least one BNN outperforms
traditional neural networks during adversarial attack scenarios. An
adversarially-trained BNN outperforms its non-Bayesian, adversarially-trained
counterpart in most experiments, and often by significant margins. Lastly, we
investigate network calibration and find that BNNs do not make overconfident
predictions, providing evidence that BNNs are also better at measuring
uncertainty.
