LightFakeDetect: A Lightweight Model for Deepfake Detection in Videos That Focuses on Facial Regions

Sarab AlMuhaideb (), Hessa Alshaya, Layan Almutairi, Danah Alomran and Sarah Turki Alhamed
Additional contact information
Sarab AlMuhaideb: Department of Computer Science, College of Computer and Information Sciences, King Saud University, P.O. Box 266, Riyadh 11362, Saudi Arabia
Hessa Alshaya: Department of Computer Science, College of Computer and Information Sciences, King Saud University, P.O. Box 266, Riyadh 11362, Saudi Arabia
Layan Almutairi: Department of Computer Science, College of Computer and Information Sciences, King Saud University, P.O. Box 266, Riyadh 11362, Saudi Arabia
Danah Alomran: Department of Computer Science, College of Computer and Information Sciences, King Saud University, P.O. Box 266, Riyadh 11362, Saudi Arabia
Sarah Turki Alhamed: Department of Computer Science, College of Computer and Information Sciences, King Saud University, P.O. Box 266, Riyadh 11362, Saudi Arabia

Mathematics, 2025, vol. 13, issue 19, 1-22

Abstract: In recent years, the proliferation of forged videos, known as deepfakes, has escalated significantly, primarily due to advancements in technologies such as Generative Adversarial Networks (GANs), diffusion models, and Vision Language Models (VLMs). These deepfakes present substantial risks, threatening political stability, facilitating celebrity impersonation, and enabling tampering with evidence. As the sophistication of deepfake technology increases, detecting these manipulated videos becomes increasingly challenging. Most of the existing deepfake detection methods use Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), or Vision Transformers (ViTs), achieving strong accuracy but exhibiting high computational demands. This highlights the need for a lightweight yet effective pipeline for real-time and resource-limited scenarios. This study introduces a lightweight deep learning model for deepfake detection in order to address this emerging threat. The model incorporates three integral components: MobileNet for feature extraction, a Convolutional Block Attention Module (CBAM) for feature enhancement, and a Gated Recurrent Unit (GRU) for temporal analysis. Additionally, a pre-trained Multi-Task Cascaded Convolutional Network (MTCNN) is utilized for face detection and cropping. The model is evaluated using the Deepfake Detection Challenge (DFDC) and Celeb-DF v2 datasets, demonstrating impressive performance, with 98.2% accuracy and a 99.0% F1-score on Celeb-DF v2 and 95.0% accuracy and a 97.2% F1-score on DFDC, achieving a commendable balance between simplicity and effectiveness.

Keywords: deepfake detection; video manipulation; deep learning; multi-task cascaded convolutional network (MTCNN) (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/19/3088/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/19/3088/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:13:y:2025:i:19:p:3088-:d:1758483

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().