A convolutional neural network strongly robust to adversarial perturbations at reasonable computational and performance cost has not yet been demonstrated. The primate visual ventral stream seems to be robust to small perturbations in visual stimuli but the underlying mechanisms that give rise to this robust perception are not understood. In this work, we investigate the role of two biologically plausible mechanisms in adversarial robustness. We demonstrate that the non-uniform sampling performed by the primate retina and the presence of multiple receptive fields with a range of receptive field sizes at each eccentricity improve the robustness of neural networks to small adversarial perturbations. We verify that these two mechanisms do not suffer from gradient obfuscation and study their contribution to adversarial robustness through ablation studies.

中文翻译：

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对抗鲁棒性的生物学启发机制

尚未证明卷积神经网络在合理的计算和性能成本下对对抗性扰动具有很强的鲁棒性。灵长类视觉腹侧流似乎对视觉刺激中的小扰动具有鲁棒性，但引起这种强烈感知的潜在机制尚不清楚。在这项工作中，我们研究了两种生物学上合理的机制在对抗鲁棒性中的作用。我们证明了灵长类动物视网膜执行的非均匀采样以及在每个偏心率处具有一系列感受野大小的多个感受野的存在提高了神经网络对小的对抗性扰动的鲁棒性。