Dissociable neural representations of adversarially perturbed images in convolutional neural networks and the human brain

TL;DR

This study compares how the human brain and CNNs process adversarial images, revealing fundamental differences in neural representations and perceptual recognition, which can guide future neural network development.

Contribution

It provides a detailed comparison of neural representations of adversarial images in humans and CNNs, highlighting key differences and suggesting directions for improving CNN models.

Findings

Humans recognize AI images as categories but see AN images as noise.

CNNs recognize AN images but misclassify AI images with high confidence.

Human brain representations align with perceptual similarity, unlike CNNs.

Abstract

Despite the remarkable similarities between convolutional neural networks (CNN) and the human brain, CNNs still fall behind humans in many visual tasks, indicating that there still exist considerable differences between the two systems. Here, we leverage adversarial noise (AN) and adversarial interference (AI) images to quantify the consistency between neural representations and perceptual outcomes in the two systems. Humans can successfully recognize AI images as corresponding categories but perceive AN images as meaningless noise. In contrast, CNNs can correctly recognize AN images but mistakenly classify AI images into wrong categories with surprisingly high confidence. We use functional magnetic resonance imaging to measure brain activity evoked by regular and adversarial images in the human brain, and compare it to the activity of artificial neurons in a prototypical CNN-AlexNet. In the human brain, we find that the representational similarity between regular and adversarial images largely echoes their perceptual similarity in all early visual areas. In AlexNet, however, the neural representations of adversarial images are inconsistent with network outputs in all intermediate processing layers, providing no neural foundations for perceptual similarity. Furthermore, we show that voxel-encoding models trained on regular images can successfully generalize to the neural responses to AI images but not AN images. These remarkable differences between the human brain and AlexNet in the representation-perception relation suggest that future CNNs should emulate both behavior and the internal neural presentations of the human brain.