论文标题
对数映射的重复注意模型对抗攻击是可靠的
Recurrent Attention Model with Log-Polar Mapping is Robust against Adversarial Attacks
论文作者
论文摘要
卷积神经网络容易受到小$ \ ell^p $对抗性攻击的影响，而人类视觉系统却不是。受眼睛和大脑中神经网络的启发，我们开发了一种新型的人工神经网络模型，该模型将其复活地收集了具有由注意力控制的对数偏置视野的数据。我们证明了这种设计的有效性，以防止SPSA和PGD对抗性攻击。它还具有在动物视觉系统中观察到的有益特性，例如低延迟推断的反射样途径，与图像大小无关的固定量计算以及旋转和比例不变性。实验代码可从https://gitlab.com/exwzd-public/kiritani_ono_2020获得。
Convolutional neural networks are vulnerable to small $\ell^p$ adversarial attacks, while the human visual system is not. Inspired by neural networks in the eye and the brain, we developed a novel artificial neural network model that recurrently collects data with a log-polar field of view that is controlled by attention. We demonstrate the effectiveness of this design as a defense against SPSA and PGD adversarial attacks. It also has beneficial properties observed in the animal visual system, such as reflex-like pathways for low-latency inference, fixed amount of computation independent of image size, and rotation and scale invariance. The code for experiments is available at https://gitlab.com/exwzd-public/kiritani_ono_2020.
