An Integrated Framework for Video-Based Deepfake Forensic Analysis

Author First name, Last name, Institution

Bashaer Mohamed Alsalami, Zayed University
Richard Ikuesan, Zayed University

Document Type

Conference Proceeding

Source of Publication

Isdfs 2025 13th International Symposium on Digital Forensics and Security

Publication Date

6-2-2025

Abstract

Deepfakes represent one of the most significant digital advancements in recent years, utilizing artificial intelligence algorithms to replicate human voices, facial expressions, and behaviors with remarkable realism and precision. These technologies are the product of significant progress in machine learning techniques, particularly deep learning. Deepfakes have since emerged as a substantial risk originally conceived for entertainment purposes. Their capacity for accurate behavioral imitation poses dangers, as they can be exploited to deceive unsuspecting individuals, prompting serious ethical and security concerns. This research aims to investigate strategies to mitigate the potential detrimental effects of deepfakes, given that current methodologies appear insufficient to uphold the integrity of digital communications.

DOI Link

10.1109/isdfs65363.2025.11012010

ISBN

[9798331509934]

Publisher

IEEE

Disciplines

Computer Sciences

Keywords

AI, algorithms, convolutional neural networks (CNNs), cybersecurity, Deepfake, Deepfake detection, forensic analysis, generative adversarial networks (GANs), machine learning, manipulation, security, synthetic media

Scopus ID

105008493895

Recommended Citation

Alsalami, Bashaer Mohamed and Ikuesan, Richard, "An Integrated Framework for Video-Based Deepfake Forensic Analysis" (2025). All Works. 7439.
https://zuscholars.zu.ac.ae/works/7439

Indexed in Scopus

yes

Open Access

no