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

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Authors: Zong Ke, Shicheng Zhou, Yining Zhou, Chia Hong Chang, Rong Zhang

Published: 2025-01-13 03:10:54+00:00

AI Summary

This research proposes a novel GAN-based model for detecting deepfakes in online payment images, enhancing security by identifying subtle manipulations. The model achieves a high detection rate above 95% by training on a dataset of real and deepfake payment images generated using StyleGAN and DeepFake.

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

Key findings

The GAN-based model achieved over 95% accuracy in distinguishing between legitimate and deepfake payment images. The model demonstrated high precision and recall, effectively minimizing both false positives and false negatives. The AUC score of 0.982 further supports the model's strong performance.

Approach

The researchers trained a GAN model, consisting of a generator and discriminator, on a dataset of real and deepfake payment images. The generator creates synthetic deepfake images, while the discriminator learns to distinguish between real and fake images. The adversarial training process helps improve the model's ability to identify subtle manipulations.

Datasets

A dataset of 5,000 real-world online payment images sourced from public datasets (Google Open Images, Kaggle, AI Benchmark Datasets, Alipay's documentation) and 5,000 deepfake images generated using StyleGAN and DeepFake.

Model(s)

Generative Adversarial Network (GAN)

Author countries

Singapore, USA

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