ResNet-GA: Evolutionary Deep Learning Models for Adversarial Defense (Student Abstract)

Abstract Adversarial attacks remain a major challenge for deep learning models, as they can undermine both performance and reliability in practical applications such as image recognition. Although evolutionary algorithms (EAs) have proven effective in optimizing complex systems, their use for directly enhancing model robustness for adversarial defense has been limited. In this study, we introduce ResNet-GA, a method that applies evolutionary deep learning (EDL) to develop ResNet-like networks specifically designed to resist different forms of adversarial perturbations. The approach evolves network architectures with a genetic algorithm (GA), adapting the Residual Blocks at every stage in ResNet according to the needs of each dataset and attack type. Experimental results show that ResNet-GA strengthens model robustness beyond standard baselines, highlighting the value of iterative evolutionary design for building more dependable deep learning systems under various adversarial conditions.