DeepFool is a simple and accurate method to fool deep neural networks. The authors propose an algorithm to efficiently compute perturbations that fool deep networks, allowing for reliable quantification of the robustness of these classifiers. The method is tested on various image classification tasks, including MNIST, CIFAR-10, and ImageNet, and outperforms existing methods in computing adversarial perturbations and improving classifier robustness. The algorithm works by iteratively linearizing the classifier and computing minimal perturbations that change the classification result. It is applicable to both binary and multiclass classifiers, and can be extended to different norms, including $\ell_2$ and $\ell_\infty$. The method is efficient and accurate, making it suitable for evaluating the robustness of deep neural networks. The results show that DeepFool generates smaller perturbations compared to other methods, leading to more accurate assessments of classifier robustness. Additionally, fine-tuning networks using DeepFool's adversarial examples significantly improves their robustness to adversarial perturbations. The study highlights the importance of accurate methods for computing adversarial perturbations in understanding the vulnerabilities of deep neural networks.DeepFool is a simple and accurate method to fool deep neural networks. The authors propose an algorithm to efficiently compute perturbations that fool deep networks, allowing for reliable quantification of the robustness of these classifiers. The method is tested on various image classification tasks, including MNIST, CIFAR-10, and ImageNet, and outperforms existing methods in computing adversarial perturbations and improving classifier robustness. The algorithm works by iteratively linearizing the classifier and computing minimal perturbations that change the classification result. It is applicable to both binary and multiclass classifiers, and can be extended to different norms, including $\ell_2$ and $\ell_\infty$. The method is efficient and accurate, making it suitable for evaluating the robustness of deep neural networks. The results show that DeepFool generates smaller perturbations compared to other methods, leading to more accurate assessments of classifier robustness. Additionally, fine-tuning networks using DeepFool's adversarial examples significantly improves their robustness to adversarial perturbations. The study highlights the importance of accurate methods for computing adversarial perturbations in understanding the vulnerabilities of deep neural networks.