Abutting Grating Illusion: Cognitive Challenge to Neural Network Models

Deep learning has achieved tremendous success during the past decade, even surpassing human performance in numerous vision tasks[Russakovsky et al., 2015][Dodge and Karam, 2017]. However, it is certainly not the panicillin to all vision tasks that humans can perform. While ANN models can achieve extremely high results on test set drawn from the same distribution of training set, they can easily fail facing with OOD(out-of-distribution) data[Dodge and Karam, 2017]. It has been discovered that neural network performance decreases under different image corruptions, such as noise, blur, brightness change, fog, etc[Dodge and Karam, 2016][Hendrycks and Dietterich, 2019]. On the other hand, humans are extremely robust to different sorts of distortions applied to images[Dodge and Karam, 2017]. An even more extreme case is adversarial attacks, where human-imperceptible perturbations could cause catastrophic failures to well-trained neural network models[Szegedy et al., 2013]. Multiple attack and defense mechanisms[Szegedy et al., 2013][Carlini and Wagner, 2017][Madry et al., 2017][Moosavi-Dezfooli et al., 2016][Papernot et al., 2016] have been proposed in recent years, but the problem still remains unsolved. Moreover, it has been found that the errors made by humans and nerual network models have little correlation with each other[Dodge and Karam, 2017], indicating that current machine visual systems might still have fundamental deficits compared to human visual systems.