NERO-Net: A Revolutionary Approach to Building Unbreakable AI Architectures

This research introduces NERO-Net, a fascinating new approach to designing Convolutional Neural Networks (CNNs) that are inherently resistant to adversarial attacks. The system employs neuroevolution to discover architectures that exhibit strong robustness, marking a significant advancement in the field of Computer Vision and AI safety. This innovative method focuses on creating models that are robust by design, rather than relying solely on adversarial training.