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

arXiv: https://arxiv.org/abs/2501.07033 · HTML: https://mlsanity.com/paper/2501.07033/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 21 references that are in the map.
- First posted on arXiv 13 Jan 2025.

## Builds on (most cited first, 7 of 7)

- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [CycleGAN, a Master of Steganography](https://mlsanity.com/paper/1712.02950/) (2017)
- [Fighting deepfakes by detecting GAN DCT anomalies](https://mlsanity.com/paper/2101.09781/) (2021)
- [Synthetic Data Generation for Fraud Detection using GANs](https://mlsanity.com/paper/2109.12546/) (2021)
- [How does Lipschitz Regularization Influence GAN Training?](https://mlsanity.com/paper/1811.09567/) (2018)
- [Using GANs to Synthesise Minimum Training Data for Deepfake Generation](https://mlsanity.com/paper/2011.05421/) (2020)

## Built on by (most cited first, 14 of 14)

- [Research on the Design of a Short Video Recommendation System Based on Multimodal Information and Differential Privacy](https://mlsanity.com/paper/2504.08751/) (2025)
- [User Behavior Analysis in Privacy Protection with Large Language Models: A Study on Privacy Preferences with Limited Data](https://mlsanity.com/paper/2505.06305/) (2025)
- [A Comprehensive Review of AI Agents: Transforming Possibilities in Technology and Beyond](https://mlsanity.com/paper/2508.11957/) (2025)
- [Credit Risk Identification in Supply Chains Using Generative Adversarial Networks](https://mlsanity.com/paper/2501.10348/) (2025)
- [Overview of the NLPCC 2025 Shared Task 4: Multi-modal, Multilingual, and Multi-hop Medical Instructional Video Question Answering Challenge](https://mlsanity.com/paper/2505.06814/) (2025)
- [Regression and Forecasting of U.S. Stock Returns Based on LSTM](https://mlsanity.com/paper/2502.05210/) (2025)
- [Large Language Model as Token Compressor and Decompressor](https://mlsanity.com/paper/2603.25340/) (2026)
- [GenoArmory: A Unified Evaluation Framework for Adversarial Attacks on Genomic Foundation Models](https://mlsanity.com/paper/2505.10983/) (2025)
- [Don't let the information slip away](https://mlsanity.com/paper/2602.22595/) (2026)
- [Hierarchical Indexing with Knowledge Enrichment for Multilingual Video Corpus Retrieval](https://mlsanity.com/paper/2510.09553/) (2025)
- [A Multi-Scale Graph Learning Framework with Temporal Consistency Constraints for Financial Fraud Detection in Transaction Networks under Non-Stationary Conditions](https://mlsanity.com/paper/2603.14592/) (2026)
- [Boosting Self-Supervised Tracking with Contextual Prompts and Noise Learning](https://mlsanity.com/paper/2605.06092/) (2026)
- [Fraud is Not Just Rarity: A Causal Prototype Attention Approach to Realistic Synthetic Oversampling](https://mlsanity.com/paper/2507.14706/) (2025)
- [Q-SYNTH: Hybrid Quantum-Classical Adversarial Augmentation for Imbalanced Fraud Detection](https://mlsanity.com/paper/2605.21164/) (2026)

## Abstract (arXiv)

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