Detection of AI Deepfake and Fraud in Online Payments Using GAN-Based Models

2025-01-13

AI Summary

To address identity spoofing fraud risks posed by deepfake faces in online payment scenarios, this paper proposes a payment-oriented two-stage GAN-based discriminator. Methodologically, it is the first to jointly leverage StyleGAN- and DeepFake-generated samples for detector training, integrating texture anomaly detection with semantic inconsistency modeling; additionally, a real-fake hybrid data augmentation strategy is introduced to enhance generalization. Experiments on a multi-source payment image dataset demonstrate an average detection accuracy of 95.3% and a false positive rate below 2.1%, significantly outperforming conventional CNNs and handcrafted-feature approaches. The main contributions are: (1) a lightweight, payment-adapted two-stage discriminative architecture; (2) a novel detection paradigm based on collaborative training with generative adversarial samples; and (3) empirical validation of cross-task transferability between GAN generation mechanisms and deepfake detection.

Technology Category

Computer Vision: Generative Adversarial Networks (GANs) for VisionNatural Language Processing: GenerationMachine Learning: Deep Generative Models & Autoencoders

Application Category

Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAISocial Networks and Social Media: Generative AI / large language models and their impact on social systemsSearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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

Problem

• Generative Adversarial Networks
• Deep Fake Detection
• Online Payment Security

Innovation

• Generative Adversarial Networks
• Deepfake Detection
• Online Payment Security