A Transfer-Learning Approach for Detection of Multiclass Synthetic Skin Cancer Images Generated by Deep Generative Models to Prevent Medical Insurance Fraud
Tariq, Osama ; Arshed, Muhammad Asad ; Kabir, Muhammad LU
; Ijaz, Khalid
; Gherghina, Ştefan Cristian
and Batool, Hafiza Bukhtawer
(2026)
In Mathematical and Computational Applications
31(1).
- Abstract
Artificial Intelligence is advancing rapidly, raising critical concerns about the integrity of digital content, particularly in sensitive domains such as medical imaging. Recent AI techniques, such as Generative Adversarial Networks (GANs) and diffusion models, can generate highly realistic synthetic medical images, posing risks of misdiagnosis, inappropriate treatment, and other adverse outcomes. This paper presents a deep learning-based approach to distinguish between authentic and synthetic images of skin malignancies generated by DCGAN, Wasserstein GAN (WGAN), and Stable Diffusion. A comprehensive dataset was constructed using authentic malignant skin images from an open-source Kaggle repository, alongside artificially generated... (More)
Artificial Intelligence is advancing rapidly, raising critical concerns about the integrity of digital content, particularly in sensitive domains such as medical imaging. Recent AI techniques, such as Generative Adversarial Networks (GANs) and diffusion models, can generate highly realistic synthetic medical images, posing risks of misdiagnosis, inappropriate treatment, and other adverse outcomes. This paper presents a deep learning-based approach to distinguish between authentic and synthetic images of skin malignancies generated by DCGAN, Wasserstein GAN (WGAN), and Stable Diffusion. A comprehensive dataset was constructed using authentic malignant skin images from an open-source Kaggle repository, alongside artificially generated images. Multiple deep learning models were trained and evaluated, with DenseNet169 achieving the highest performance, reaching 99.67% training accuracy, 97.50% validation accuracy, and 98.50% test accuracy—along with substantial precision, recall, and F1 scores across all classes. These results demonstrate the model’s efficacy in identifying both real and fake medical images. This work contributes to the emerging field of medical image forensics, highlighting its potential integration into clinical and insurance workflows to prevent fraud, strengthen trust, and mitigate risks. Furthermore, it lays the groundwork for future studies involving larger datasets, additional Deepfake generation methods, and real-time clinical applications.
(Less)
- author
- Tariq, Osama
; Arshed, Muhammad Asad
; Kabir, Muhammad
LU
; Ijaz, Khalid
; Gherghina, Ştefan Cristian
and Batool, Hafiza Bukhtawer
- organization
- publishing date
- 2026-02
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- DCGAN, deep learning, medical image analysis, skin cancer, stable diffusion, synthetic images, transfer learning, WGAN
- in
- Mathematical and Computational Applications
- volume
- 31
- issue
- 1
- article number
- 31
- publisher
- MDPI AG
- external identifiers
-
- scopus:105031556601
- ISSN
- 1300-686X
- DOI
- 10.3390/mca31010031
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: © 2026 by the authors.
- id
- a8b0ff53-d755-4130-91c7-8b28f3bacb17
- date added to LUP
- 2026-04-09 15:08:07
- date last changed
- 2026-04-09 15:19:02
@article{a8b0ff53-d755-4130-91c7-8b28f3bacb17,
abstract = {{<p>Artificial Intelligence is advancing rapidly, raising critical concerns about the integrity of digital content, particularly in sensitive domains such as medical imaging. Recent AI techniques, such as Generative Adversarial Networks (GANs) and diffusion models, can generate highly realistic synthetic medical images, posing risks of misdiagnosis, inappropriate treatment, and other adverse outcomes. This paper presents a deep learning-based approach to distinguish between authentic and synthetic images of skin malignancies generated by DCGAN, Wasserstein GAN (WGAN), and Stable Diffusion. A comprehensive dataset was constructed using authentic malignant skin images from an open-source Kaggle repository, alongside artificially generated images. Multiple deep learning models were trained and evaluated, with DenseNet169 achieving the highest performance, reaching 99.67% training accuracy, 97.50% validation accuracy, and 98.50% test accuracy—along with substantial precision, recall, and F1 scores across all classes. These results demonstrate the model’s efficacy in identifying both real and fake medical images. This work contributes to the emerging field of medical image forensics, highlighting its potential integration into clinical and insurance workflows to prevent fraud, strengthen trust, and mitigate risks. Furthermore, it lays the groundwork for future studies involving larger datasets, additional Deepfake generation methods, and real-time clinical applications.</p>}},
author = {{Tariq, Osama and Arshed, Muhammad Asad and Kabir, Muhammad and Ijaz, Khalid and Gherghina, Ştefan Cristian and Batool, Hafiza Bukhtawer}},
issn = {{1300-686X}},
keywords = {{DCGAN; deep learning; medical image analysis; skin cancer; stable diffusion; synthetic images; transfer learning; WGAN}},
language = {{eng}},
number = {{1}},
publisher = {{MDPI AG}},
series = {{Mathematical and Computational Applications}},
title = {{A Transfer-Learning Approach for Detection of Multiclass Synthetic Skin Cancer Images Generated by Deep Generative Models to Prevent Medical Insurance Fraud}},
url = {{http://dx.doi.org/10.3390/mca31010031}},
doi = {{10.3390/mca31010031}},
volume = {{31}},
year = {{2026}},
}