This study explores the use of Generative Adversarial Networks (GANs) to detect AI deepfakes and fraudulent activities in online payment systems. With the growing prevalence of deepfake technology, which can manipulate facial features in images and videos, the potential for fraud in online transactions has escalated. Traditional security systems struggle to identify these sophisticated forms of fraud. This research proposes a novel GAN-based model that enhances online payment security by identifying subtle manipulations in payment images. The model is trained on a dataset consisting of real-world online payment images and deepfake images generated using advanced GAN architectures, such as StyleGAN and DeepFake. The results demonstrate that the proposed model can accurately distinguish between legitimate transactions and deepfakes, achieving a high detection rate above 95%. This approach significantly improves the robustness of payment systems against AI-driven fraud. The paper contributes to the growing field of digital security, offering insights into the application of GANs for fraud detection in financial services. Keywords- Payment Security, Image Recognition, Generative Adversarial Networks, AI Deepfake, Fraudulent Activities

翻译：本研究探讨了利用生成对抗网络（GAN）检测在线支付系统中的AI深度伪造与欺诈活动。随着能够操纵图像和视频中面部特征的深度伪造技术日益普及，在线交易中的欺诈风险显著上升。传统安全系统难以识别此类复杂的欺诈形式。本研究提出了一种新颖的基于GAN的模型，通过识别支付图像中的细微篡改来增强在线支付安全性。该模型使用包含真实在线支付图像以及通过StyleGAN和DeepFake等先进GAN架构生成的深度伪造图像的数据集进行训练。实验结果表明，所提模型能准确区分合法交易与深度伪造内容，检测率高达95%以上。该方法显著提升了支付系统对抗AI驱动欺诈的鲁棒性。本文为数字安全领域的发展做出贡献，为GAN在金融服务欺诈检测中的应用提供了新的见解。关键词- 支付安全、图像识别、生成对抗网络、AI深度伪造、欺诈活动