Lateralized learning for robustness against adversarial attacks in a visual classification system

Deep learning is an important field of machine learning. It is playing a critical role in a variety of applications ranging from self-driving cars to security and surveillance. However, deep networks have deep flaws. For example, they are highly vulnerable to adversarial attacks. One reason may be the homogeneous nature of their knowledge representation, which allows a single disruptive pattern to cause miss-classification. Biological intelligence has lateral asymmetry, which allows heterogeneous, modular learning at different levels of abstraction, enabling different representations of the same object. This work aims to incorporate lateralization and modular learning at different levels of abstraction in an evolutionary machine learning system. The results of image classification tasks show that the lateralized system efficiently learns hierarchical distributions of knowledge, demonstrating performance that is similar to (or better than) other state-of-the-art deep systems as it reasons using multiple representations. Crucially, the novel system outperformed all the state-of-the-art deep models for the classification of normal and adversarial images by 0.43% - 2.56% and 2.15% - 25.84%, respectively. Lateralisation enabled the system to exhibit robustness beyond previous work, which advocates for the creation of data sets that enable components of objects and the objects themselves to be learned specifically or in an end-to-end manner.</p