This paper explores the vulnerability of Deep Neural Networks (DNNs) to one-pixel adversarial attacks, a scenario where only a single pixel in an image can be modified. The authors propose a novel method using Differential Evolution (DE) to generate such perturbations, which requires less adversarial information and can fool more types of networks. The results show that 67.97% of natural images in the Kaggle CIFAR-10 test dataset and 16.04% of ImageNet test images can be perturbed to at least one target class with high confidence. The attack is effective, semi-black-box, and flexible, making it a valuable tool for evaluating DNN robustness. The paper also discusses the geometrical insights gained from this extreme scenario and the implications for understanding DNN input spaces. The proposed method is superior to random attacks and has potential applications in generating adversarial images for training more robust models.This paper explores the vulnerability of Deep Neural Networks (DNNs) to one-pixel adversarial attacks, a scenario where only a single pixel in an image can be modified. The authors propose a novel method using Differential Evolution (DE) to generate such perturbations, which requires less adversarial information and can fool more types of networks. The results show that 67.97% of natural images in the Kaggle CIFAR-10 test dataset and 16.04% of ImageNet test images can be perturbed to at least one target class with high confidence. The attack is effective, semi-black-box, and flexible, making it a valuable tool for evaluating DNN robustness. The paper also discusses the geometrical insights gained from this extreme scenario and the implications for understanding DNN input spaces. The proposed method is superior to random attacks and has potential applications in generating adversarial images for training more robust models.