Recurrent Attention Model with Log-Polar Mapping is Robust against Adversarial Attacks

• URL: http://arxiv.org/abs/2002.05388v1
• Date: Thu, 13 Feb 2020 08:40:48 GMT
• Title: Recurrent Attention Model with Log-Polar Mapping is Robust against Adversarial Attacks
• Authors: Taro Kiritani, Koji Ono
• Abstract summary: We develop a novel artificial neural network model that recurrently collects data with a log-polar field of view controlled by attention. We demonstrate the effectiveness of this design as a defense against SPSA and PGD adversarial attacks.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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