Topic: adversarial attacks

Object recognition relies on the complex visual representations in cortical areas at the top of the ventral stream hierarchy. While these are thought to be derived from low-level stages of visual processing, this has not been shown, yet. Here, I describe the results of two projects exploring the contributions of primary visual cortex (V1) processing to object recognition using artificial neural networks (ANNs). First, we developed hundreds of ANN-based V1 models and evaluated how their single neurons approximate those in the macaque V1. We found that, for some models, single neurons in interme

Deep neural networks (DNNs) are extremely vulnerable to adversarial attacks, where small changes to the input, unnoticeable to humans, can drastically alter the model’s predictions. One reason for their poor performance is that they process as much information as possible and make decisions based on the input received. Human vision performs differently. It tries be as efficient with information as possible while using predictions to fill in missing information based on existing knowledge. The brain may even suppress sensory input to save energy if it is familiar (i.e., predictable). As the kn