Degree
Master of Science in Data Science
Department
Department of Computer Science
Faculty/ School
School of Mathematics and Computer Science (SMCS)
Date of Submission
Spring 2024
Supervisor
Dr. Faisal Iradat, Assistant Professor, Department of Computer Science, School of Mathematics and Computer Science (SMCS)
Keywords
Deepfake Detection, Deep Learning, Convolutional Neural Networks (CNNs), Video Classification
Abstract
The increasing prevalence of deepfake videos, created using advanced deep learning techniques, poses a significant threat to the authenticity and reliability of visual media. This project aims to develop a sophisticated deepfake detection system utilizing cutting-edge deep learning algorithms. Specifically, we employ convolutional neural networks (CNNs) to construct a robust model capable of distinguishing between authentic and manipulated videos with high accuracy.
Through rigorous training and evaluation on a comprehensive dataset containing both genuine and deepfake videos, our system demonstrates promising results in accurately identifying deepfakes. This project contributes to the ongoing efforts to combat the spread of misinformation and reinforces the integrity of digital media in the age of deepfakes.
Document Type
Restricted Access
Submission Type
Research Project
Recommended Citation
Ikram, S. (2024). Authentiscan: Deepfake Detection and Authentication (Unpublished graduate research project). Institute of Business Administration, Pakistan. Retrieved from https://ir.iba.edu.pk/research-projects-msds/31
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