Journal Article

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

Arshed, Muhammad Asad, Tanveer, Muhammad, Ibrahim, Muhammad, Dewi, Christine, Ahmed, Saeed, Mumtaz, Shahzad

2024

Overview

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.

• Publisher: MDPI AG
• Subject: Accuracy / Algorithms / Analysis / Annan data- och informationsvetenskap / artificial intelligence / Artificial neural networks / Classification / CNN / Computational linguistics / Computer and Information Sciences / Context / Data- och informationsvetenskap (Datateknik) / Datasets / Deception / Deep learning / deepfake identification / Face recognition / Forensic sciences / Generative adversarial networks / Identification / Image detection / Image processing / Language processing / Machine learning / Methods / Multimedia / Natural language interfaces / Natural Sciences / Naturvetenskap / Neural networks / Other Computer and Information Science / Performance evaluation / Safety and security measures / Social media / Social networks / stable diffusion