The article explores a new method to reduce overfitting in machine learning called robust overfitting. By using data augmentation techniques in a unique way, the researchers were able to improve the accuracy of their model when faced with adversarial attacks. Their experiments on CIFAR-10 and CIFAR-100 datasets showed significant enhancements in robust accuracy compared to previous methods. The strongest adversarial example generated through this approach led to a notable increase in robust accuracy, outperforming other state-of-the-art models in the same conditions.