  Convolutional neural networks (CNNs) have achieved great success in natural
image saliency prediction. The primary goal of this study is to investigate the
performance of saliency prediction in CNN and classic models with
psychophysical synthetic images under noise perturbation. Is it still as decent
as natural images in terms of performance? In the meantime, it can be used to
investigate the relationship between CNNs and human vision, mainly low-level
vision functions. On the other hand, are CNNs exact replicas of human visual
function? This study used CNNs, Fourier, and spectral models inspired by
low-level vision systems to investigate saliency prediction on psychophysical
synthetic images rather than natural images. According to our findings,
saliency prediction models inspired by Fourier and spectral theory outperformed
current pre-trained deep neural networks on psychophysical images with noise
perturbation. However, psychophysical models were more unstable in noise than
pre-trained deep neural networks. Meanwhile, we suggested that investigating
CNNs with psychophysical methods could benefit visual neuroscience and
artificial neural network studies.
