Google DeepMind: Subtle Adversarial Image Manipulation Influences Both AI Model and Human Perception

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Recent research by Google DeepMind has revealed a surprising intersection between human and machine vision, particularly in their susceptibility to adversarial images. Adversarial images are digital images subtly altered to deceive AI models, making them misclassify the image contents. For example, a vase could be misclassified as a cat by the AI​​.

The study published in «Nature Communications» titled «Subtle adversarial image manipulations influence both human and machine perception» conducted a series of experiments to investigate the impact of adversarial images on human perception. These experiments found that while adversarial perturbations significantly mislead machines, they can also subtly influence human perception. Notably, the effect on human decision-making was consistent with the misclassifications made by AI models, albeit not as pronounced. This discovery underlines the nuanced relationship between human and machine vision, showing that both can be influenced by minor perturbations in an image, even if the perturbation magnitudes are small and the viewing times are extended​​​​.

DeepMind's research also explored the properties of artificial neural network (ANN) models that contribute to this susceptibility. They studied two ANN architectures: convolutional networks and self-attention architectures. Convolutional networks, inspired by the primate visual system, apply static local filters across the visual field, building a hierarchical representation. In contrast, self-attention architectures, originally designed for natural language processing, use nonlocal operations for global communication across the entire image space, showing a stronger bias toward shape features than texture features. These models