Multiclass AI-Generated Deepfake Face Detection Using Patch-Wise Deep Learning Model

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Arshed, Muhammad AsadUniversity of Management and Technology, Lahore (författare)

Multiclass AI-Generated Deepfake Face Detection Using Patch-Wise Deep Learning Model

Artikel/kapitelEngelska2024

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2024

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LIBRIS-ID:oai:lup.lub.lu.se:16cea7af-858d-47c4-8104-9f42033d312f
https://lup.lub.lu.se/record/16cea7af-858d-47c4-8104-9f42033d312fURI
https://doi.org/10.3390/computers13010031DOI

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Språk:engelska
Sammanfattning på:engelska

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Ämneskategori:ref swepub-contenttype

Anmärkningar

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.

Ämnesord och genrebeteckningar

NATURVETENSKAP Data- och informationsvetenskap Annan data- och informationsvetenskap hsv//swe
NATURAL SCIENCES Computer and Information Sciences Other Computer and Information Science hsv//eng
artificial intelligence
CNN
deep learning
deepfake identification
global feature extraction
image processing
patches
stable diffusion
StyleGAN2
vision transformer

Biuppslag (personer, institutioner, konferenser, titlar ...)

Mumtaz, ShahzadThe Islamia University of Bahawalpur (författare)
Ibrahim, MuhammadThe Islamia University of Bahawalpur (författare)
Dewi, ChristineSatya Wacana Christian University (författare)
Tanveer, Muhammad (författare)
Ahmed, SaeedLund University,Lunds universitet,Proteinbioinformatik,Forskargrupper vid Lunds universitet,Protein Bioinformatics,Lund University Research Groups,University of Management and Technology, Lahore(Swepub:lu)sa4634ah (författare)
University of Management and Technology, LahoreThe Islamia University of Bahawalpur (creator_code:org_t)

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Ingår i:Computers13:12073-431X

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Av författaren/redakt...: Arshed, Muhammad ...; Mumtaz, Shahzad; Ibrahim, Muhamma ...; Dewi, Christine; Tanveer, Muhamma ...; Ahmed, Saeed

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NATURVETENSKAP: NATURVETENSKAP; och Data och informa ...; och Annan data och i ...

Artiklar i publikationen: Computers

Av lärosätet: Lunds universitet

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