Digital Face Forgery and the Role of Digital Forensics

Tampubolon, Manotar

doi:10.1007/s11196-023-10030-1

Cited by 3 publications

(1 citation statement)

References 38 publications

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“…By developing advanced approaches, they aim to safeguard individuals and organizations from the potential harms posed by this evolving technology [12]. This involves harnessing the progress made in computer vision, machine learning, and forensic analysis to detect crucial indicators of image manipulation and effectively differentiate between authentic and manipulated facial images [13].…”

Section: Introductionmentioning

confidence: 99%

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

Arshed,

Mumtaz,

Ibrahim

et al. 2024

Computers

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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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Section: Introductionmentioning

confidence: 99%

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

Arshed,

Mumtaz,

Ibrahim

et al. 2024

Computers

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Abuse of Rhizomatic Internet Memes: Disruptive Non-Verbal Discourse

Wagner,

Yu,

Marusek

2024

Law and Visual Jurisprudence

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The Language of Romance Crimes

Carter

2024

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Exploring the interplay of love, money and threat in romance fraud, this Element reveals how language is used to persuade, manipulate, and threaten without causing alarm. It provides the first empirical examination of criminal interactions-in-action that exposes and tracks the grooming process and manipulation techniques from first contact with the fraudster, to the transition between romance and finance, and requests for money and intimate images, before morphing into explicit threats and acts of sextortion. Through the use of a range of interactional methodologies and real romance fraud messages, a new type of criminality in the form of 'romance fraud enabled sextortion' is revealed. The insights contained in this work have clear implications for future directions of academic exploration and practitioner efforts to protect the public. This title is also available as Open Access on Cambridge Core.

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Digital Face Forgery and the Role of Digital Forensics

Cited by 3 publications

References 38 publications

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

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

Abuse of Rhizomatic Internet Memes: Disruptive Non-Verbal Discourse

The Language of Romance Crimes

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