Anomaly detection is a critical challenge across various research domains, aiming to identify instances that deviate from normal data distributions. This paper explores the application of Generative Adversarial Networks (GANs) in fraud detection, comparing their advantages with traditional methods. GANs, a type of Artificial Neural Network (ANN), have shown promise in modeling complex data distributions, making them effective tools for anomaly detection. The paper systematically describes the principles of GANs and their derivative models, emphasizing their application in fraud detection across different datasets. And by building a collection of adversarial verification graphs, we will effectively prevent fraud caused by bots or automated systems and ensure that the users in the transaction are real. The objective of the experiment is to design and implement a fake face verification code and fraud detection system based on Generative Adversarial network (GANs) algorithm to enhance the security of the transaction process.The study demonstrates the potential of GANs in enhancing transaction security through deep learning techniques.

翻译：异常检测是各个研究领域面临的关键挑战，旨在识别偏离正常数据分布的实例。本文探讨了生成对抗网络（GANs）在欺诈检测中的应用，并比较了其与传统方法的优势。作为人工神经网络（ANN）的一种，GANs在建模复杂数据分布方面展现出潜力，使其成为异常检测的有效工具。本文系统描述了GANs及其衍生模型的原理，重点阐述了它们在不同数据集上欺诈检测中的应用。此外，通过构建对抗性验证图谱，我们将有效防止机器人或自动化系统引发的欺诈行为，确保交易中的用户为真实用户。实验的目标是基于生成对抗网络（GANs）算法，设计并实现一种假脸验证码与欺诈检测系统，以提升交易流程的安全性。研究结果表明，GANs通过深度学习技术在增强交易安全性方面具有显著潜力。