Source-linked AI summary
Detection of AI Deepfake and Fraud in Online Payments Using GAN-Based Models
Zong Ke, Shicheng Zhou, Yining Zhou, Chia Hong Chang, Rong Zhang
TL;DR
Deepfakes pose a security threat to online payments, and traditional payment-security solutions do not recognize that threat. The paper applies GANs to detect deepfake-related fraud in payment images, reporting strong test-set metrics, while noting open challenges in complex contexts and cross-domain generalization.
Problem
Deepfakes are described as a severe online-payment security threat that traditional payment-security solutions do not recognize.
Source: §I · p. 1Method
The paper investigates GAN-based detection of fraudulent deepfakes in online-payment images using natural and artificial deepfake images.
Source: §II · p. 1Results
The reported test results were 96.20% accuracy, 95.50% precision, 96.80% recall, 96.10% F1-Score, and 98.20% AUC.
Source: §IV · p. 4Takeaways & Limitations
The authors conclude that their GAN-based online-payment security scheme detects AI-based deepfakes and fake behavior and greatly improves payment safety.
Source: §V · p. 4The authors identify complex contexts, cross-domain generalization, real-time payment systems, edge cases, and sophisticated fraud patterns as areas needing further work.
Source: §V · p. 4
Abstract
from arXiv · showhide
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
I. INTRODUCTION AND LITERATURE REVIEW
Deepfakes have moved from entertainment and social media toward malicious uses that threaten online payments, while traditional payment security solutions do not recognize this threat. The paper proposes applying GANs to detect this activity and presents the work as a novel use of AI algorithms against these threats.
I. INTRODUCTION AND LITERATURE REVIEW
- Deepfakes’ potential malicious use in online scams and identity theft is framed as a severe online-payment security threat that traditional solutions do not recognize.The paper describes deepfakes as AI-based face-manipulation technologies and notes StyleGAN’s use in photorealistic image rendering, face manipulation, and forgery detection.Source: §I · p. 1
- The authors present their work as, to their knowledge, the first to show AI algorithms tackling malicious threats associated with advancing technology.Source: §I · p. 1
- GAN variants including DCGAN, CycleGAN, and StyleGAN have improved rendered-image quality, robustness, and diversity, with StyleGAN producing high-resolution photorealistic images.The paper connects StyleGAN with applications including AI face manipulation and forgery detection.Source: §I · p. 1
II. OBJECT AND SUBJECT OF RESEARCH
The study investigates whether GANs can detect fraudulent deepfake activity in online-payment images. Its objective is a model for identifying AI-generated deepfakes and distinguishing genuine from counterfeit payment images across alternative scenarios.
II. OBJECT AND SUBJECT OF RESEARCH
- The study investigates GAN effectiveness for detecting fraudulent deepfakes in online-payment images using natural and artificial deepfake images.Source: §II · p. 1
- The proposed GAN-based model aims to identify AI-generated deepfake images in online payment systems and enhance fraud detection.Source: §II · p. 1
- The work assesses distinguishing genuine from counterfeit payment images across alternative scenarios, with the aim of facilitating better payment-security architecture.Source: §II · p. 2
A. Data
The dataset combines real online-payment images with AI-generated deepfakes, and includes varied faces and both legal transactions and attack cases. Figure 1 is identified as showing distribution characteristics of the real-payment images.
A. Data
- The dataset contains 5,000 real-world payment images from online sources and 5,000 fake images generated with DeepFake and StyleGAN.The named real-image sources include Open Images, Kaggle, AI Benchmark Datasets, and Alipay open-API examples.Source: A. Data · p. 2
- Table 1 is labeled as presenting descriptive statistics.Source: Table 1 · p. 2
- Figure 1 is identified as showing distribution characteristics of the real-payment images.Source: Figure 1 · p. 2
- The dataset includes facial images spanning different ages, genders, and races, along with legal transactions and attack cases.The authors describe this mix as a balanced basis for examining the detection scheme.Source: A. Data · p. 2
B. Model design
The model uses a generator and discriminator in an adversarial GAN setup to synthesize deepfake images and distinguish real from fake payment images. It trains the two components with cross-entropy loss and alternating optimization.
B. Model design
- The generator creates fictitious deepfake images from random noise, while the discriminator classifies input images as real payment images or AI-generated fakes.Their adversarial training is intended to improve generation and real/fake identification.Source: Figure 3 · p. 3, B. Model design · p. 3
- The model uses the classical cross-entropy loss and alternately optimizes the generator and discriminator over multiple epochs.Training uses Adam for 10K iterations, with learning rate and batch size adjusted to balance the components.Source: Figure 3 · p. 3, B. Model design · p. 3
- The generator maps noise input z to a synthetic image, while the discriminator aims to classify real images as true and generated images as fake.The adversarial minimization balances the generator’s image replication with the discriminator’s classification.Source: B. Model design · p. 3
IV. RESULTS
The model reports strong test-set scores across accuracy, precision, recall, F1-score, and AUC, alongside validation accuracy above 95%. The results section also presents model results and a confusion matrix for fraud detection.
IV. RESULTS
- More than 95% validation accuracy was reported for distinguishing genuine payment images from fake deepfakes.The generator produced very realistic deepfake images in the reported test of generator and discriminator performance.Source: §IV · p. 3
- Figure 4 is presented as model results, and the evaluation uses accuracy, precision, and recall to assess authentic and fraudulent payment images.The text describes the GAN-based model as accurate and resilient in identifying deepfakes and fraudulent online payments.Source: §IV · pp. 3–4, Figure 4 · p. 4
- Accuracy 96.20%, Precision 95.50%, Recall 96.80%, F1-Score 96.10%, and AUC 98.20% were reported for the test dataset.The table reports these five evaluation metrics for the GAN-based model.Source: §IV · p. 4
- The experiments report that the GAN-based model efficiently detects fraudulent deepfakes for online payments, with Figure 5 identified as its confusion matrix.The accompanying text connects this detection result with improved electronic-business security.Source: §IV · p. 4, Figure 5 · p. 4
V. CONCLUSIONS
The paper presents a GAN-based scheme for detecting AI deepfakes and fake behavior in online payments. It identifies complex contexts and cross-domain generalization as remaining challenges.
V. CONCLUSIONS
- The paper proposes a GAN-based online payment security scheme for detecting AI-based deepfakes and fake behavior.Its conclusion reports excellent accuracy and time efficiency and says the model greatly improves online-payment safety.Source: §V · p. 4
- Detecting fraud in complex contexts and improving generalization to different domains remain challenges for the model.The authors also identify multimodal data, real-time payment systems, and edge cases as topics for further study.Source: §V · p. 4