Analyzing Deepfake Detection Methods
Master of Science in Computer Science
Faculty / School
Faculty of Computer Sciences (FCS)
Department of Computer Science
Date of Award
Dr. Muhammad Sarim.
Committee Member 1
Dr. Muhammad Sarim, Supervisor: .MS-Computer Science
MSCS Survey Report
Autoencoder, Algorithms, Deepfake, Artificial intelligence--Social aspects
Abstract / Summary
Deep learning has rate of success in solving various different complex problems including data analytics, human level control and computer vision. However, the advancement in deep learning have created different software which has caused many threats to privacy and security. The most recent and popular application of deep learning is deepfake. Fake images and fake videos are being created using Deepfake algorithms which are very difficult to distinguish from original at human level. Recently many researchers have started working on detecting deepfakes image and videos, they have suggested different models and algorithms which automatically distinguish from originals. This paper reviews different research papers which suggest different algorithms which are used for creation and, more importantly, it also reviews methods and algorithms suggested for deepfake detection proposed in literature. As many of the research papers and articles are reviewed for background of deepfake, its creation and detection. This paper provides a very comprehensive understanding and overview of deepfake techniques. this examine presents a complete assessment of different deepfake algorithms and techniques. Which allows the development of latest and greater robust strategies to cope with the more and more challenging deepfaked images or videos.
Memon, M. A. (2019). Analyzing Deepfake Detection Methods (Unpublished survey report). Retrieved from https://ir.iba.edu.pk/research-projects/31
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