用GAN模型识别支付图像中的AI换脸欺诈，准确率超95%
Detection of AI Deepfake and Fraud in Online Payments Using GAN-Based Models
- 基于StyleGAN等生成对抗网络构建检测模型
- 在真实与伪造支付图上测试，识别准确率超95%
- 适合金融安全、AI防御领域研究人员参考
本研究探索使用生成对抗网络（GAN）检测在线支付系统中的AI深度伪造和欺诈行为。随着深度伪造技术发展，图像和视频中的人脸特征可被操纵，导致在线交易欺诈风险上升。传统安全系统难以识别此类复杂欺诈。本文提出一种新型GAN模型，通过训练真实支付图像与基于StyleGAN和DeepFake生成的伪造图像数据集，提升支付安全。实验表明，该模型能有效区分合法交易与深度伪造，检测准确率超过95%。该方法显著增强了支付系统对人工智能驱动欺诈的抵御能力。研究为数字安全领域提供新思路，推动GAN在金融服务欺诈检测中的应用。
原文摘要 · Abstract (English)
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
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