# You Only Attack Once: Single-Step DeepFool Algorithm
## 1 Introduction  
With the rapid advancement of computer technology, deep learning has found extensive applications in various domains, particularly excelling in tasks like image classification. However, their high accuracy and robustness can be compromised by adversaries who deliberately manipulate the input data to deceive the model. Such attacks could have severe implications in real-world applications, such as in autonomous driving systems [1], where adversarial attacks could mislead the vehicle's AI perception system, causing it to overlook obstacles or misinterpret road signs, leading to serious consequences. Alternatively, they can deceive security systems employing deep neural networks (DNNs) for face recognition [2]. Attackers may carry out such actions with malicious intent to cause harm or to exploit the artificial intelligence system for personal gain. Adversarial attacks are not only present in the field of computer vision but also exist in other areas, such as quantum cryptography attacks [3, 4]. Understanding the vulnerabilities of DNNs toadversarial attacks and developing effective defense mechanisms has become a crucial research area in the field of artificial intelligence (AI) and computer security. Adversarial samples are constructed by introducing minor perturbations to the original data, which trick the classifier into making incorrect classifications. By investigating the generation algorithms of adversarial examples, research can advance defenses against such examples, consequently bolstering the security of deep neural network models.  
Adversarial sample generation algorithms play a crucial role in understanding the limitations of neural networks and enhancing network security. A growing number of startups are involved in a diverse range of computer vision tasks. DeepFool is an algorithm proposed by [PERSON] et al. [5] that iteratively determines the minimum perturbation needed to move a given input image into a misclassified region of the feature space. Although the DeepFool generation process typically requires multiple iterations to achieve high attack success rates, conducting numerous iterations to generate adversarial samples substantially escalates computational complexity. This necessitates greater computational resources, particularly in the present era characterized by rapid growth in big data and large-scale models. For startups, this presents a significant challenge in improving model security. Therefore, a pressing need exists for a rapid and efficient method to seek adversarial perturbations, to bolster adversarial training techniques, fortify model security, and investigate model robustness against adversarial perturbations. Within the domain of adversarial attacks, methods for generating adversarial samples through single-step iteration remain relatively scarce. In this study, we modified the iteration direction of the DeepFool algorithm. By leveraging gradient differences to identify the direction of samples closest to the decision boundary and searching for adversarial samples along this direction, we can accelerate the generation speed of adversarial samples.  
While the DeepFool algorithm is effective in identifying minimal perturbations for misclassifying data, a more expedited methodology is imperative. Our main contributions are as follows:  
* As shown in Figure 1, We enhanced the DeepFool algorithm by computing the gradient information of each class in input samples and comparing these gradient differences with the true class that generates adversarial samples in a single iteration, achieving a significant 68% increase in the success rate of adversarial sample generation compared to the original algorithm.
* We demonstrated through experiments that the algorithm has a simpler time complexity than the DeepFool algorithm. By conducting comparative experiments with other related research, we showed that the YOAO algorithm exhibits superior attack efficiency and is more effective at misleading various deep neural networks into misclassification.