  The majority of computer vision algorithms fail to find higher-order
(abstract) patterns in an image so are not robust against adversarial attacks,
unlike human lateralized vision. Deep learning considers each input pixel in a
homogeneous manner such that different parts of a ``locality-sensitive hashing
table'' are often not connected, meaning higher-order patterns are not
discovered. Hence these systems are not robust against noisy, irrelevant, and
redundant data, resulting in the wrong prediction being made with high
confidence. Conversely, vertebrate brains afford heterogeneous knowledge
representation through lateralization, enabling modular learning at different
levels of abstraction. This work aims to verify the effectiveness, scalability,
and robustness of a lateralized approach to real-world problems that contain
noisy, irrelevant, and redundant data. The experimental results of multi-class
(200 classes) image classification show that the novel system effectively
learns knowledge representation at multiple levels of abstraction making it
more robust than other state-of-the-art techniques. Crucially, the novel
lateralized system outperformed all the state-of-the-art deep learning-based
systems for the classification of normal and adversarial images by 19.05% -
41.02% and 1.36% - 49.22%, respectively. Findings demonstrate the value of
heterogeneous and lateralized learning for computer vision applications.
