  The security of Deep Learning classifiers is a critical field of study
because of the existence of adversarial attacks. Such attacks usually rely on
the principle of transferability, where an adversarial example crafted on a
surrogate classifier tends to mislead the target classifier trained on the same
dataset even if both classifiers have quite different architecture. Ensemble
methods against adversarial attacks demonstrate that an adversarial example is
less likely to mislead multiple classifiers in an ensemble having diverse
decision boundaries. However, recent ensemble methods have either been shown to
be vulnerable to stronger adversaries or shown to lack an end-to-end
evaluation. This paper attempts to develop a new ensemble methodology that
constructs multiple diverse classifiers using a Pairwise Adversarially Robust
Loss (PARL) function during the training procedure. PARL utilizes gradients of
each layer with respect to input in every classifier within the ensemble
simultaneously. The proposed training procedure enables PARL to achieve higher
robustness against black-box transfer attacks compared to previous ensemble
methods without adversely affecting the accuracy of clean examples. We also
evaluate the robustness in the presence of white-box attacks, where adversarial
examples are crafted using parameters of the target classifier. We present
extensive experiments using standard image classification datasets like
CIFAR-10 and CIFAR-100 trained using standard ResNet20 classifier against
state-of-the-art adversarial attacks to demonstrate the robustness of the
proposed ensemble methodology.
