  Artificial Neural Networks (ANNs) trained with Backpropagation (BP) excel in
different daily tasks but have a dangerous vulnerability: inputs with small
targeted perturbations, also known as adversarial samples, can drastically
disrupt their performance. Adversarial training, a technique in which the
training dataset is augmented with exemplary adversarial samples, is proven to
mitigate this problem but comes at a high computational cost. In contrast to
ANNs, humans are not susceptible to misclassifying these same adversarial
samples. Thus, one can postulate that biologically-plausible trained ANNs might
be more robust against adversarial attacks. In this work, we chose the
biologically-plausible learning algorithm Present the Error to Perturb the
Input To modulate Activity (PEPITA) as a case study and investigated this
question through a comparative analysis with BP-trained ANNs on various
computer vision tasks. We observe that PEPITA has a higher intrinsic
adversarial robustness and, when adversarially trained, also has a more
favorable natural-vs-adversarial performance trade-off. In particular, for the
same natural accuracies on the MNIST task, PEPITA's adversarial accuracies
decrease on average only by 0.26% while BP's decrease by 8.05%.
