الصفحة 1
الصفحة 1
Multimedia Forensics
Media forensics has never been more relevant to societal life. Not only media content represents an ever-increasing share of the data traveling on the net and the preferred communications means for most users, it has also become integral part of most innovative applications in the digital information ecosystem that serves various sectors of society, from the entertainment, to journalism, to politics. Undoubtedly, the advances in deep learning and computational imaging contributed significantly to this outcome. The underlying technologies that drive this trend, however, also pose a profound challenge in establishing trust in what we see, hear, and read, and make media content the preferred target of malicious attacks.
Hydra = هايدرا
Forgery involves the use of advanced algorithms to replicate and distribute deceptive products across various categories, casting shadows of doubt on the authenticity of goods. Although counterfeit detection can be useful in identifying and mitigating fraudulent activities, the widespread presence of counterfeit goods poses significant dangers, undermining consumer confidence and brand reputation. To underscore the severity of this issue, consider instances such as fake luxury items flooding the market, counterfeit electronics compromising safety, or bogus pharmaceuticals endangering health. Addressing this issue is critical in maintaining the integrity of brands, safeguarding consumer well-being, and preserving trust in the marketplace. The ability to distinguish between authentic and counterfeit products is paramount in ensuring accurate decision-making and preventing the harmful consequences of fraudulent goods. This technological context underscores the urgency of developing and deploying cutting-edge solutions to combat the evolving landscape of product forgery. Hydra emerges as a robust solution, utilizing a comprehensive approach that includes extracting posts and images from search engine tools, and is integrated with AI models to detect forgery. The Hydra platform not only provides users with a powerful tool for detecting counterfeit products but also offers tangible benefits such as enhanced brand security, increased awareness about the prevalence of forgeries, and the opportunity to actively participate in a real-time community.
Handbook of Digital Face Manipulation and Detection: From DeepFakes to Morphing Attacks
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.
Founders at Work : Stories of Startups’ Early Days
Founders at Work: Stories of Startups' Early Days is a collection of interviews with founders of famous technology companies about what happened in the very earliest days. These people are celebrities now. What was it like when they were just a couple friends with an idea? Founders like Steve Wozniak (Apple), Caterina Fake (Flickr), Mitch Kapor (Lotus), Max Levchin (PayPal), and Sabeer Bhatia (Hotmail) tell you in their own words about their surprising and often very funny discoveries as they learned how to build a company.
E-Democracy – Safeguarding Democracy and Human Rights in the Digital Age ; 8th International Conference, e-Democracy 2019, Athens, Greece, December 12-13, 2019, Proceedings
This book constitutes the refereed proceedings of the 8th International Conference on E-Democracy, E-Democracy 2019, held in Athens, Greece, in December 2019. The 15 revised full papers presented were carefully selected from 27 submissions. The papers are organized in topical sections on rrivacy and data protection; e-government; e-voting and forensics; online social networks and "fake news".
Deepfake detection = اكتشاف التزييف العميق
In the rapidly evolving era of artificial intelligence, addressing the escalating threats of deepfake technology becomes a necessity because of the increasing sophistication of AI algorithms in generating deceptive content, and since it threatens the integrity of information across diverse data. The main objective is to build a sophisticated AI-driven system to detect different types of deepfake in text, audio, and images. In English text deepfake detection, multiple pre-trained tokenizers have been used, but XLNET and BERT stand out with identifying objects outside the dataset with an accuracy of 0.9809 and both have been generalized & trained using LSTM. In Arabic text deepfake detection, Arabert has been trained using LSTM which led with an accuracy of 99.53% by generalizing the model. Both English and Arabic datasets have been generated to enhance the accuracy and effectiveness of the models. Audio deepfake detection has been generalized too, using Random Forest with an accuracy of 98.259%.
Deepfake detection
Recently, various techniques of manipulating the video content have become available to everyone – online, one can find free applications e.g., for face swapping in videos. Such universal accessibility carries a notable risk of flooding online content with false information, affecting not only the greats of this world, but also the whole societies, also the rapid progress in synthetic image generation and manipulation has now come to a point where it raises significant concerns for the implications towards society. It is therefore necessary to develop a verification tool that will help assess the authenticity of the videos posted on the internet. This project describes the approach of using artificial intelligence solutions to detect doctored videos.
Deepfake detection
The rise of large language models (LLMs) and the increasing sophistication of deepfake images have made detecting synthetic content a pressing challenge. Several approaches have been proposed to tackle this problem, including statistical analysis, and machine learning algorithms. In this project, A novel zero-shot approach is proposed that utilizes the power of LLMs to detect fake text. The pre-trained LLM is fine-tuned to enhance its ability to differentiate real and fake text. The approach uses the LLM to detect text by analyzing the log probabilities of the text. For detecting fake images, computer vision algorithms and neural networks are used to analyze facial features. The facial region is cropped and preprocessed and the neural network identifies patterns indicative of synthetic content.
Deepfake
The technology used to create such digital content has quickly become accessible to the masses, such as “DEEPFAKE.” Deep fakes refer to manipulated videos, or other digital representations produced by sophisticated artificial intelligence, that yields to synthesize a sequence of face images and voices of characters corresponding to their identities, such as voice tone, facial expression, with a good lip synchronization. Therefore, this study is about developing real-time video generation software, which generates a target video from a single input image. Several methods and algorithms have been applied to detect, analyze personalize facial expression, voice and natural head poses to present a life-like image instead of a low quality one.
Deep Learning and its Applications
Presents an introduction to deep learning and various applications of deep learning such as recommendation systems, text recognition, diabetic retinopathy prediction of breast cancer, prediction of epilepsy, sentiment, fake news detection, software defect prediction and protein function prediction.
Deep fake detection
Deep learning has been successfully applied to solve various complex problems ranging from big data analytics to computer vision and human-level control. Deep learning advances however have also been employed to create software that can cause threats to privacy, democracy and national security. One of those deep learning-powered applications recently emerged is “deepfake”. Deepfake algorithms can create fake images and videos that humans cannot distinguish them from authentic ones. The proposal of technologies that can automatically detect and assess the integrity of digital visual media is therefore indispensable.
Automorphic Forms and Lie Superalgebras
Most known examples of Lie superalgebras with a related automorphic form such as the Fake Monster Lie algebra whose reflection group is given by the Leech lattice arise from (super)string theory and can be derived from lattice vertex algebras. The No-Ghost Theorem from dual resonance theory and a conjecture of Berger-Li-Sarnak on the eigenvalues of the hyperbolic Laplacian provide strong evidence that they are of rank at most 26.The aim of this book is to give the reader the tools to understand the ongoing classification and construction project of this class of Lie superalgebras and is ideal for a graduate course.
Artificial intelligence and national security
Analyses the implications of the technical, legal, ethical and privacy challenges as well as challenges for human rights and civil liberties regarding Artificial Intelligence (AI) and National Security. It also offers solutions that can be adopted to mitigate or eradicate these challenges wherever possible. As a general-purpose, dual-use technology, AI can be deployed for both good and evil. The use of AI is increasingly becoming of paramount importance to the governments mission to keep their nations safe. However, the design, development and use of AI for national security poses a wide range of legal, ethical, moral and privacy challenges. This book explores national security uses for Artificial Intelligence (AI) in Western Democracies and its malicious use. This book also investigates the legal, political, ethical, moral, privacy and human rights implications of the national security uses of AI in the aforementioned democracies. It illustrates how AI for national security purposes could threaten most individual fundamental rights, and how the use of AI in digital policing could undermine user human rights and privacy.
Arabic talking heads
The need for someone to talk to about what is going on inside us while ensuring privacy has become a necessary requirement, especially since some people in our Arab societies judge people who visit a psychiatrist as mentally ill. Therefore, in this project, we propose a solution to the previous problem, where an Arabic-speaking robot, through an interactive chat application can mitigate the psychological and negative effects that humans are exposed to in various cases.
Advancement of Deep Learning and its Applications in Object Detection and Recognition
In just the past five years, deep learning has taken the world by surprise, driving rapid progress in fields as diverse as computer vision, natural language processing, automatic speech recognition, etc. This book presents an introduction to deep learning and various applications of deep learning such as recommendation systems, text recognition, diabetic retinopathy prediction of breast cancer, prediction of epilepsy, sentiment, fake news detection, software defect prediction and protein function prediction.
