Artificial vision has often been described as one of the key remaining challenges to be solved before machines can act intelligently. Recent developments in a branch of machine learning known as deep learning have catalyzed impressive gains in machine vision—giving a sense that the problem of vision is getting closer to being solved. The goal of this review is to provide a comprehensive overview of recent deep learning developments and to critically assess actual progress toward achieving human-level visual intelligence. I discuss the implications of the successes and limitations of modern machine vision algorithms for biological vision and the prospect for neuroscience to inform the design of future artificial vision systems.
Artificial vision systems are susceptible to adversarial attacks. Smallintentional changes to images can cause these systems to mis-classify with high confidence. The brain has many mechanisms forstrengthening weak or confusing inputs. One such technique, con-tour integration can separate objects from irrelevant background.We show that incorporating contour integration within artificial vi-sual systems can increase their robustness to adversarial attacks.
Guarding Against Adversarial Attacks using Biologically Inspired Contour Integration
