Artificial Neural Networks (ANNs) trained with Backpropagation (BP) show
astounding performance and are increasingly often used in performing our daily
life tasks. However, ANNs are highly vulnerable to adversarial attacks, which
alter inputs with small targeted perturbations that drastically disrupt the
models' performance. The most effective method to make ANNs robust against
these attacks is adversarial training, in which the training dataset is
augmented with exemplary adversarial samples. Unfortunately, this approach has
the drawback of increased training complexity since generating adversarial
samples is very computationally demanding. In contrast to ANNs, humans are not
susceptible to adversarial attacks. Therefore, in this work, we investigate
whether biologically-plausible learning algorithms are more robust against
adversarial attacks than BP. In particular, we present an extensive comparative
analysis of the adversarial robustness of BP and \textit{Present the Error to
Perturb the Input To modulate Activity} (PEPITA), a recently proposed
biologically-plausible learning algorithm, on various computer vision tasks. We
observe that PEPITA has higher intrinsic adversarial robustness and, with
adversarial training, has a more favourable natural-vs-adversarial performance
trade-off as, for the same natural accuracies, PEPITA's adversarial accuracies
decrease in average by 0.26% and BP's by 8.05%.

比较研究了 BP 算法和 PEPITA 算法在计算机视觉任务中对抗攻击的鲁棒性，结果显示 PEPITA 算法具有更高的内在对抗鲁棒性，并且在相同自然准确率下，其对抗准确率平均下降 0.26%，而 BP 算法下降 8.05%。