Professor James Elder, who is a co-author of a study published by York University, says that deep convolutional neural networks (DCNNs) do not perceive objects as humans do, with configural shape perception, which could be risky in real-time AI applications. The study was reported in the iScience -- a Cell Press journal. "Deep Learning Models Are Unsuccessful in Capturing the Configural Manner of Human Shape Perception" is a joint study by Elder, a York Research Chair in Human and Computer Vision and a Co-Director of York's Centre for AI & Society, and Nicholas Baker, an Assistant Psychology Professor at Loyola College in Chicago, a former VISTA postdoctoral fellow at York. To discover how the human brain and DCNNs process complete, configural object properties, the scientists used novel visual stimuli known as "Frankensteins." Frankensteins are simply objects that have been taken apart and put back together the wrong way around.

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Machine learning models are vulnerable to adversarial examples: small changes to images can cause computer vision models to make mistakes such as identifying a school bus as an ostrich. However, it is still an open question whether humans are prone to similar mistakes. Here, we create the first adversarial examples designed to fool humans, by leveraging recent techniques that transfer adversarial examples from computer vision models with known parameters and architecture to other models with unknown parameters and architecture, and by modifying models to more closely match the initial processing of the human visual system. We find that adversarial examples that strongly transfer across computer vision models influence the classifications made by time-limited human observers.