Ultimate Deepfake Detection Using Python
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| Author | : Dr. Nimrita Koul |
| Publisher | : Orange Education Pvt Ltd |
| Total Pages | : 291 |
| Release | : 2024-09-21 |
| Genre | : Computers |
| ISBN | : 8197953422 |
TAGLINE Deepfake Detection Unlocked: Python Approaches for Deepfake Images, Videos, Audio Detection. KEY FEATURES ● Comprehensive and graded approach to Deepfake detection using Python and its libraries. ● Practical implementation of deepfake detection techniques using Python. ● Hands-on chapters for detecting deepfake images, videos, and audio. ● Covers Case study for providing real-world application of deepfake detection. DESCRIPTION In today's digital world, mastering deepfake detection is crucial, with deepfake content increasing by 900% since 2019 and 96% used for malicious purposes like fraud and disinformation. "Ultimate Deepfake Detection with Python" equips you with the skills to combat this threat using Python’s AI libraries, offering practical tools to protect digital security across images, videos, and audio. This book explores generative AI and deepfakes, giving readers a clear understanding of how these technologies work and the challenges of detecting them. With practical Python code examples, it provides the tools necessary for effective deepfake detection across media types like images, videos, and audio. Each chapter covers vital topics, from setting up Python environments to using key datasets and advanced deep learning techniques. Perfect for researchers, developers, and cybersecurity professionals, this book enhances technical skills and deepens awareness of the ethical issues around deepfakes. Whether building new detection systems or improving current ones, this book offers expert strategies to stay ahead in digital media security. WHAT WILL YOU LEARN ● Understand the fundamentals of generative AI and deepfake technology and the potential risks they pose. ● Explore the various methods and techniques used to identify deepfakes, as well as the obstacles faced in this field. ● Learn to use essential datasets and label image, video, and audio data for building deepfake detection models. ● Apply advanced machine learning models like CNNs, RNNs, GANs, and Transformers for deepfake detection. ● Master active and passive methods for detecting face manipulation and build CNN-based image detection systems. ● Detect manipulations in videos, develop a detection system, and evaluate its performance using key metrics. ● Build and implement a practical deepfake detection system to understand how these techniques are applied in real-world scenarios. WHO IS THIS BOOK FOR? This book is tailored for anyone interested in deepfake detection using Python. Whether you're a researcher, developer, or cybersecurity professional, this guide provides the essential knowledge and skills. A basic understanding of Python and machine learning is helpful, but no prior experience in deepfakes is required. TABLE OF CONTENTS 1. Introduction to Generative AI and Deepfake Technology 2. Deepfake Detection Principles and Challenges 3. Ethical Considerations with the Use of Deepfakes 4. Setting Up your Machine for Deepfake Detection using Python 5. Deepfake Datasets 6. Techniques for Deepfake Detection 7. Detection of Deepfake Images 8. Detection of Deepfake Video 9. Detection of Deepfake Audio 10. Case Study in Deepfake Detection Index
| Author | : Obaid, Ahmed J. |
| Publisher | : IGI Global |
| Total Pages | : 409 |
| Release | : 2023-01-03 |
| Genre | : Computers |
| ISBN | : 1668460629 |
In recent years, falsification and digital modification of video clips, images, as well as textual contents have become widespread and numerous, especially when deepfake technologies are adopted in many sources. Due to adopted deepfake techniques, a lot of content currently cannot be recognized from its original sources. As a result, the field of study previously devoted to general multimedia forensics has been revived. The Handbook of Research on Advanced Practical Approaches to Deepfake Detection and Applications discusses the recent techniques and applications of illustration, generation, and detection of deepfake content in multimedia. It introduces the techniques and gives an overview of deepfake applications, types of deepfakes, the algorithms and applications used in deepfakes, recent challenges and problems, and practical applications to identify, generate, and detect deepfakes. Covering topics such as anomaly detection, intrusion detection, and security enhancement, this major reference work is a comprehensive resource for cyber security specialists, government officials, law enforcement, business leaders, students and faculty of higher education, librarians, researchers, and academicians.
| Author | : Christian Rathgeb |
| Publisher | : Springer Nature |
| Total Pages | : 487 |
| Release | : 2022-01-31 |
| Genre | : Computers |
| ISBN | : 3030876640 |
This open access book provides the first comprehensive collection of studies dealing with the hot topic of digital face manipulation such as DeepFakes, Face Morphing, or Reenactment. It combines the research fields of biometrics and media forensics including contributions from academia and industry. Appealing to a broad readership, introductory chapters provide a comprehensive overview of the topic, which address readers wishing to gain a brief overview of the state-of-the-art. Subsequent chapters, which delve deeper into various research challenges, are oriented towards advanced readers. Moreover, the book provides a good starting point for young researchers as well as a reference guide pointing at further literature. Hence, the primary readership is academic institutions and industry currently involved in digital face manipulation and detection. The book could easily be used as a recommended text for courses in image processing, machine learning, media forensics, biometrics, and the general security area.
| Author | : Chakchai So In |
| Publisher | : Springer Nature |
| Total Pages | : 609 |
| Release | : |
| Genre | : |
| ISBN | : 9819986125 |
| Author | : Tanwar, Poonam |
| Publisher | : IGI Global |
| Total Pages | : 240 |
| Release | : 2021-06-25 |
| Genre | : Computers |
| ISBN | : 1799877302 |
To sustain and stay at the top of the market and give absolute comfort to the consumers, industries are using different strategies and technologies. Natural language processing (NLP) is a technology widely penetrating the market, irrespective of the industry and domains. It is extensively applied in businesses today, and it is the buzzword in every engineer’s life. NLP can be implemented in all those areas where artificial intelligence is applicable either by simplifying the communication process or by refining and analyzing information. Neural machine translation has improved the imitation of professional translations over the years. When applied in neural machine translation, NLP helps educate neural machine networks. This can be used by industries to translate low-impact content including emails, regulatory texts, etc. Such machine translation tools speed up communication with partners while enriching other business interactions. Deep Natural Language Processing and AI Applications for Industry 5.0 provides innovative research on the latest findings, ideas, and applications in fields of interest that fall under the scope of NLP including computational linguistics, deep NLP, web analysis, sentiments analysis for business, and industry perspective. This book covers a wide range of topics such as deep learning, deepfakes, text mining, blockchain technology, and more, making it a crucial text for anyone interested in NLP and artificial intelligence, including academicians, researchers, professionals, industry experts, business analysts, data scientists, data analysts, healthcare system designers, intelligent system designers, practitioners, and students.
| Author | : Dr Thaddeus Eze |
| Publisher | : Academic Conferences Inter Ltd |
| Total Pages | : |
| Release | : 2021-06-24 |
| Genre | : History |
| ISBN | : 1912764431 |
Conferences Proceedings of 20th European Conference on Cyber Warfare and Security
| Author | : Bin Sheng |
| Publisher | : Springer Nature |
| Total Pages | : 518 |
| Release | : 2023-12-28 |
| Genre | : Computers |
| ISBN | : 3031500725 |
This 4-volume set of LNCS 14495-14498 constitutes the proceedings of the 40th Computer Graphics International Conference, CGI 2023, held in Shanghai, China, August 28 – September 1, 2023. The 149 papers in this set were carefully reviewed and selected from 385 submissions. They are organized in topical sections as follows: Detection and Recognition; Image Analysis and Processing; Image Restoration and Enhancement; Image Attention and Perception; Reconstruction; Rendering and Animation; Synthesis and Generation; Visual Analytics and Modeling; Graphics and AR/VR; Medical Imaging and Robotics; Theoretical Analysis; Image Analysis and Visualization in Advanced Medical Imaging Technology; Empowering Novel Geometric Algebra for Graphics and Engineering.
| Author | : Nikhil Ketkar |
| Publisher | : Apress |
| Total Pages | : 306 |
| Release | : 2021-04-10 |
| Genre | : Computers |
| ISBN | : 9781484253632 |
Master the practical aspects of implementing deep learning solutions with PyTorch, using a hands-on approach to understanding both theory and practice. This updated edition will prepare you for applying deep learning to real world problems with a sound theoretical foundation and practical know-how with PyTorch, a platform developed by Facebook’s Artificial Intelligence Research Group. You'll start with a perspective on how and why deep learning with PyTorch has emerged as an path-breaking framework with a set of tools and techniques to solve real-world problems. Next, the book will ground you with the mathematical fundamentals of linear algebra, vector calculus, probability and optimization. Having established this foundation, you'll move on to key components and functionality of PyTorch including layers, loss functions and optimization algorithms. You'll also gain an understanding of Graphical Processing Unit (GPU) based computation, which is essential for training deep learning models. All the key architectures in deep learning are covered, including feedforward networks, convolution neural networks, recurrent neural networks, long short-term memory networks, autoencoders and generative adversarial networks. Backed by a number of tricks of the trade for training and optimizing deep learning models, this edition of Deep Learning with Python explains the best practices in taking these models to production with PyTorch. What You'll Learn Review machine learning fundamentals such as overfitting, underfitting, and regularization. Understand deep learning fundamentals such as feed-forward networks, convolution neural networks, recurrent neural networks, automatic differentiation, and stochastic gradient descent. Apply in-depth linear algebra with PyTorch Explore PyTorch fundamentals and its building blocks Work with tuning and optimizing models Who This Book Is For Beginners with a working knowledge of Python who want to understand Deep Learning in a practical, hands-on manner.
| Author | : Abbas M. Al-Bakry |
| Publisher | : Springer Nature |
| Total Pages | : 422 |
| Release | : |
| Genre | : |
| ISBN | : 3031628144 |
| Author | : Albert J. Marcella |
| Publisher | : CRC Press |
| Total Pages | : 351 |
| Release | : 2021-09-13 |
| Genre | : Computers |
| ISBN | : 1000424936 |
Threat actors, be they cyber criminals, terrorists, hacktivists or disgruntled employees, are employing sophisticated attack techniques and anti-forensics tools to cover their attacks and breach attempts. As emerging and hybrid technologies continue to influence daily business decisions, the proactive use of cyber forensics to better assess the risks that the exploitation of these technologies pose to enterprise-wide operations is rapidly becoming a strategic business objective. This book moves beyond the typical, technical approach to discussing cyber forensics processes and procedures. Instead, the authors examine how cyber forensics can be applied to identifying, collecting, and examining evidential data from emerging and hybrid technologies, while taking steps to proactively manage the influence and impact, as well as the policy and governance aspects of these technologies and their effect on business operations. A world-class team of cyber forensics researchers, investigators, practitioners and law enforcement professionals have come together to provide the reader with insights and recommendations into the proactive application of cyber forensic methodologies and procedures to both protect data and to identify digital evidence related to the misuse of these data. This book is an essential guide for both the technical and non-technical executive, manager, attorney, auditor, and general practitioner who is seeking an authoritative source on how cyber forensics may be applied to both evidential data collection and to proactively managing today’s and tomorrow’s emerging and hybrid technologies. The book will also serve as a primary or supplemental text in both under- and post-graduate academic programs addressing information, operational and emerging technologies, cyber forensics, networks, cloud computing and cybersecurity.
