On Inherent Adversarial Robustness of Active Vision Systems

arXiv:2404.00185v1 Announce Type: new
Abstract: Current Deep Neural Networks are vulnerable to adversarial examples, which alter their predictions by adding carefully crafted noise. Since human eyes are robust to such inputs, it is possible that the vulnerability stems from the standard way of processing inputs in one shot by processing every pixel with the same importance. In contrast, neuroscience suggests that the human vision system can differentiate salient features by (1) switching between multiple fixation points (saccades) and (2) processing …

abstract adversarial adversarial examples arxiv cs.ai cs.cv current every examples human inputs networks neural networks noise pixel predictions processing robust robustness standard systems type vision vulnerability vulnerable