  Deploying convolutional neural networks (CNNs) for embedded applications
presents many challenges in balancing resource-efficiency and task-related
accuracy. These two aspects have been well-researched in the field of CNN
compression. In real-world applications, a third important aspect comes into
play, namely the robustness of the CNN. In this paper, we thoroughly study the
robustness of uncompressed, distilled, pruned and binarized neural networks
against white-box and black-box adversarial attacks (FGSM, PGD, C&W, DeepFool,
LocalSearch and GenAttack). These new insights facilitate defensive training
schemes or reactive filtering methods, where the attack is detected and the
input is discarded and/or cleaned. Experimental results are shown for distilled
CNNs, agent-based state-of-the-art pruned models, and binarized neural networks
(BNNs) such as XNOR-Net and ABC-Net, trained on CIFAR-10 and ImageNet datasets.
We present evaluation methods to simplify the comparison between CNNs under
different attack schemes using loss/accuracy levels, stress-strain graphs,
box-plots and class activation mapping (CAM). Our analysis reveals susceptible
behavior of uncompressed and pruned CNNs against all kinds of attacks. The
distilled models exhibit their strength against all white box attacks with an
exception of C&W. Furthermore, binary neural networks exhibit resilient
behavior compared to their baselines and other compressed variants.
