DeepFakes
Creation, Detection, and Impact
1st Edition
Published on 8. September 2022
166 pages
978-1-000-64947-5 (ISBN)
Description
Deepfakes is a synthetic media that leverage powerful Artificial Intelligence (AI) and machine learning (ML) techniques to generate fake visual and audio content that are extremely realistic, thus making it very hard for a human to distinguish from the original ones. Apart from technological introduction to the Deepfakes concept, the book details algorithms to detect Deepfakes, techniques for identifying manipulated content and identifying face swap, generative adversarial neural networks, media forensic techniques, deep learning architectures, forensic analysis of DeepFakes and so forth.
Provides a technical introduction to DeepFakes, its benefits, and the potential harms
Presents practical approaches of creation and detection of DeepFakes using Deep Learning (DL) Techniques
Draws attention towards various challenging issues and societal impact of DeepFakes with their existing solutions
Includes research analysis in the domain of DL fakes for assisting the creation and detection of DeepFakes applications
Discusses future research directions with emergence of DeepFakes technology
This book is aimed at graduate students, researchers and professionals in data science, artificial intelligence, computer vision, and machine learning.
Provides a technical introduction to DeepFakes, its benefits, and the potential harms
Presents practical approaches of creation and detection of DeepFakes using Deep Learning (DL) Techniques
Draws attention towards various challenging issues and societal impact of DeepFakes with their existing solutions
Includes research analysis in the domain of DL fakes for assisting the creation and detection of DeepFakes applications
Discusses future research directions with emergence of DeepFakes technology
This book is aimed at graduate students, researchers and professionals in data science, artificial intelligence, computer vision, and machine learning.
More details
Edition
1. Auflage
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
Professional and scholarly
Illustrations
16 Tables, black and white; 13 Line drawings, black and white; 22 Halftones, black and white; 35 Illustrations, black and white
ISBN-13
978-1-000-64947-5 (9781000649475)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Person
Loveleen Gaur is the Professor and Program Director (Artificial Intelligence and Business Intelligence and Data Analytics of the Amity International Business School, Amity University, Noida, India. She is senior IEEE member and Series Editor with CRC and Wiley. Prof Gaur is an established Author, Researcher, she has filed five patents and two copyrights in AI-IoT. For over 18 years she served in India and abroad in different capacities. Prof. Gaur has significantly contributed to enhancing scientific understanding by participating in over three hundred scientific conferences, symposia, and seminars, by chairing technical sessions and delivering plenary and invited talks. She has specialized in the fields of Artificial Intelligence, Internet of Things, Data Analytics, Data Mining and Business Intelligence. Prof. Gaur pursued research in truly inter-disciplinary areas and authored and co-authored Books with renowned International and National publishers like Elsevier, Springer, Taylor & Francis. She is invited as Guest Editor for Springer NASA Journal and Emerald Q1 journals. She has published many research papers in SCI and Q1 Journals. She has chaired various positions in International Conferences of repute and is a reviewer with top rated journals of IEEE, SCI and ABDC Journals. Prof Gaur has been invited for AICTE sponsored FDPs and workshops in Industry 4.0, Big Data, Data Analytics and Artificial Intelligence for IITs, NITs and reputed central universities. Prof Gaur is keynote speaker for several IEEE international conferences globally, external examiner/evaluator for PhD, Guest editor of several reputed journals, member of the editorial board of several research journals, and active TPC member of reputed conferences around the globe.
Content
1. Introduction to DeepFake technologies 2. DeepFakes: A Systematic Review and Bibliometric Analysis 3. Deep Learning Techniques for Creation of DeepFakes 4. Analyzing DeepFakes Videos by face warping artifacts 5. Development of image translating model to counter Adversarial attacks 6. Detection of DeepFakes using local features and Convolutional Neural Network 7. DeepFakes: Positive Cases 8. Threats and challenges by DeepFake Technology 9. DeepFakes, media, and societal impacts 10. Fake News Detection using Machine Learning 11. Future of DeepFakes & Ectypes
