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.

翻译：大多数计算机视觉算法无法在图像中提取高阶（抽象）模式，因此与人类侧化视觉不同，它们对对抗性攻击缺乏鲁棒性。深度学习以同质化方式处理每个输入像素，导致“局部敏感哈希表”的不同部分往往互不关联，从而无法发现高阶模式。因此，这些系统对含噪声、不相关及冗余的数据缺乏鲁棒性，常以高置信度给出错误预测。相比之下，脊椎动物大脑通过侧化机制实现异质性知识表征，支持不同抽象层级的模块化学习。本研究旨在验证侧化方法在包含噪声、不相关及冗余数据的实际问题中的有效性、可扩展性与鲁棒性。针对200类图像的多类别分类实验结果表明，该新型系统能有效学习多抽象层级的知识表征，其鲁棒性优于其他最先进技术。关键的是，新型侧化系统在正常图像与对抗图像分类中，分别以19.05%-41.02%和1.36%-49.22%的优势超越所有基于深度学习的最先进系统。研究结果证实了异质性与侧化学习在计算机视觉应用中的价值。