Multiclass AI-Generated Deepfake Face Detection Using Patch-Wise Deep Learning Model
(2024) In Computers 13(1).- Abstract
In response to the rapid advancements in facial manipulation technologies, particularly facilitated by Generative Adversarial Networks (GANs) and Stable Diffusion-based methods, this paper explores the critical issue of deepfake content creation. The increasing accessibility of these tools necessitates robust detection methods to curb potential misuse. In this context, this paper investigates the potential of Vision Transformers (ViTs) for effective deepfake image detection, leveraging their capacity to extract global features. Objective: The primary goal of this study is to assess the viability of ViTs in detecting multiclass deepfake images compared to traditional Convolutional Neural Network (CNN)-based models. By framing the... (More)
In response to the rapid advancements in facial manipulation technologies, particularly facilitated by Generative Adversarial Networks (GANs) and Stable Diffusion-based methods, this paper explores the critical issue of deepfake content creation. The increasing accessibility of these tools necessitates robust detection methods to curb potential misuse. In this context, this paper investigates the potential of Vision Transformers (ViTs) for effective deepfake image detection, leveraging their capacity to extract global features. Objective: The primary goal of this study is to assess the viability of ViTs in detecting multiclass deepfake images compared to traditional Convolutional Neural Network (CNN)-based models. By framing the deepfake problem as a multiclass task, this research introduces a novel approach, considering the challenges posed by Stable Diffusion and StyleGAN2. The objective is to enhance understanding and efficacy in detecting manipulated content within a multiclass context. Novelty: This research distinguishes itself by approaching the deepfake detection problem as a multiclass task, introducing new challenges associated with Stable Diffusion and StyleGAN2. The study pioneers the exploration of ViTs in this domain, emphasizing their potential to extract global features for enhanced detection accuracy. The novelty lies in addressing the evolving landscape of deepfake creation and manipulation. Results and Conclusion: Through extensive experiments, the proposed method exhibits high effectiveness, achieving impressive detection accuracy, precision, and recall, and an F1 rate of 99.90% on a multiclass-prepared dataset. The results underscore the significant potential of ViTs in contributing to a more secure digital landscape by robustly addressing the challenges posed by deepfake content, particularly in the presence of Stable Diffusion and StyleGAN2. The proposed model outperformed when compared with state-of-the-art CNN-based models, i.e., ResNet-50 and VGG-16.
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- author
- Arshed, Muhammad Asad ; Mumtaz, Shahzad ; Ibrahim, Muhammad ; Dewi, Christine ; Tanveer, Muhammad and Ahmed, Saeed LU
- organization
- publishing date
- 2024-01
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- artificial intelligence, CNN, deep learning, deepfake identification, global feature extraction, image processing, patches, stable diffusion, StyleGAN2, vision transformer
- in
- Computers
- volume
- 13
- issue
- 1
- article number
- 31
- publisher
- MDPI AG
- external identifiers
-
- scopus:85183387021
- ISSN
- 2073-431X
- DOI
- 10.3390/computers13010031
- language
- English
- LU publication?
- yes
- id
- 16cea7af-858d-47c4-8104-9f42033d312f
- date added to LUP
- 2024-02-20 12:49:15
- date last changed
- 2024-02-20 12:49:35
@article{16cea7af-858d-47c4-8104-9f42033d312f,
abstract = {{<p>In response to the rapid advancements in facial manipulation technologies, particularly facilitated by Generative Adversarial Networks (GANs) and Stable Diffusion-based methods, this paper explores the critical issue of deepfake content creation. The increasing accessibility of these tools necessitates robust detection methods to curb potential misuse. In this context, this paper investigates the potential of Vision Transformers (ViTs) for effective deepfake image detection, leveraging their capacity to extract global features. Objective: The primary goal of this study is to assess the viability of ViTs in detecting multiclass deepfake images compared to traditional Convolutional Neural Network (CNN)-based models. By framing the deepfake problem as a multiclass task, this research introduces a novel approach, considering the challenges posed by Stable Diffusion and StyleGAN2. The objective is to enhance understanding and efficacy in detecting manipulated content within a multiclass context. Novelty: This research distinguishes itself by approaching the deepfake detection problem as a multiclass task, introducing new challenges associated with Stable Diffusion and StyleGAN2. The study pioneers the exploration of ViTs in this domain, emphasizing their potential to extract global features for enhanced detection accuracy. The novelty lies in addressing the evolving landscape of deepfake creation and manipulation. Results and Conclusion: Through extensive experiments, the proposed method exhibits high effectiveness, achieving impressive detection accuracy, precision, and recall, and an F1 rate of 99.90% on a multiclass-prepared dataset. The results underscore the significant potential of ViTs in contributing to a more secure digital landscape by robustly addressing the challenges posed by deepfake content, particularly in the presence of Stable Diffusion and StyleGAN2. The proposed model outperformed when compared with state-of-the-art CNN-based models, i.e., ResNet-50 and VGG-16.</p>}},
author = {{Arshed, Muhammad Asad and Mumtaz, Shahzad and Ibrahim, Muhammad and Dewi, Christine and Tanveer, Muhammad and Ahmed, Saeed}},
issn = {{2073-431X}},
keywords = {{artificial intelligence; CNN; deep learning; deepfake identification; global feature extraction; image processing; patches; stable diffusion; StyleGAN2; vision transformer}},
language = {{eng}},
number = {{1}},
publisher = {{MDPI AG}},
series = {{Computers}},
title = {{Multiclass AI-Generated Deepfake Face Detection Using Patch-Wise Deep Learning Model}},
url = {{http://dx.doi.org/10.3390/computers13010031}},
doi = {{10.3390/computers13010031}},
volume = {{13}},
year = {{2024}},
}