الصفحة 10
الصفحة 10
Digital watermarking ; 6th International Workshop, IWDW 2007 Guangzhou, China, December 3-5, 2007 Proceedings
This book constitutes the refereed proceedings of the 6th International Workshop, IWDW 2007, held in Guangzhou, China, in December 2007.
Digital Rights Management : Technologies, Issues, Challenges and Systems
The First International Conference on Digital Rights Management: Technology, Issues, Challenges and Systems (DRMTICS – pronounced ‘dramatics’), took place in Sydney, Australia on 31st October - 2nd November, 2005.DRMTICS isan internationalconferenceseriesthat coversthe areaof digital rights management, including research advancements of an applied and theor- ical nature. The goal is to have a broad coverage of the ?eld and related issues and subjects as the area evolves. Since the Internet and the computing infr- tructure has turned into a marketplace for content where information goods of various kinds are exchanged, this area is expected to grow and be part of the ongoing evolution of the information society.
Digital Mammography ; 9th International Workshop, IWDM 2008 Tucson, AZ, USA, July 20-23, 2008 Proceedings
This volume (5116) of Springer’s Lecture Notes in Computer Science contains the th proceedings of the 9 International Workshop on Digital Mammography (IWDM) which was held July 20 – 23, 2008 in Tucson, AZ in the USA.
Digital Mammography ; 8th International Workshop, IWDM 2006, Manchester, UK, June 18-21, 2006, Proceedings
This volume of Springer’s Lecture Notes in Computer Science series records th the proceedings of the 8 International Workshop on Digital Mammography (IWDM), which was held in Manchester, UK, June 18–21, 2006. The meetings bringtogetheradiversesetofresearchers(physicists,mathematicians,computer scientists, engineers), clinicians (radiologists, surgeons) and representatives of industry, who are jointly committed to developing technology, not just for its ownsake,but to supportclinicians inthe earlydetection andsubsequentpatient management of breast cancer.
Digital Image Processing : An Algorithmic Introduction using Java
This modern, self-contained, textbook explains the fundamental algorithms of digital image processing through practical examples and complete Java implementations.
Digital image processing
Completely self-contained and heavily illustrated, this introduction to basic concepts and methodologies for digital image processing is written at a level that is suitable for seniors and first-year graduate students in almost any technical discipline
Digital Image Processing
The book offers an integral view of image processing from image acquisition to the extraction of the data of interest. The discussion of the general concepts is supplemented with examples from applications on PC-based image processing systems and ready-to-use implementations of important algorithms. Each chapter now includes exercises that help you to test your understanding, train your skills, and introduce you to real-world image processing tasks. An important part of the exercises is a wealth of interactive computer exercises, which cover all topics of this textbook.
Digital human modeling and applications in health, safety, ergonomics and risk management : Posture, motion and health ; 11th International Conference, DHM 2020, Held as Part of the 22nd HCI International Conference, HCII 2020, Copenhagen, Denmark, July 19–24, 2020, Proceedings, Part I
This two-volume set LNCS 12198 and 12199 constitutes the thoroughly refereed proceedings of the 11th International Conference on Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management, DHM 2020, which was supposed to be held as part of the 22st HCI International Conference, HCII 2020, in Copenhagen, Denmark, in July 2020. The conference was held virtually due to the COVID-19 pandemic.
Digital human modeling and applications in health, safety, ergonomics and risk management : Human communication, organization and work ; 11th International Conference, DHM 2020, Held as Part of the 22nd HCI International Conference, HCII 2020, Copenhagen, Denmark, July 19–24, 2020, Proceedings, Part II
This two-volume set LNCS 12198 and 12199 constitutes the thoroughly refereed proceedings of the 11th International Conference on Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management, DHM 2020, which was supposed to be held as part of the 22st HCI International Conference, HCII 2020, in Copenhagen, Denmark, in July 2020. The conference was held virtually due to the COVID-19 pandemic.
Digital Human Modeling ; 1st International Conference, ICDHM 2007, Held as Part of HCI International 2007, Beijing, China, July 22-27, 2007, Proceedings
Theis book includes human aspects of design and use of computing systems.It covers the entire field of Human-Computer Interaction, addressing major - vances in knowledge and effective use of computers in a variety of application areas. It also contains thematic area of Digital Human Modeling, Shape and Movement Modeling and Anthropometry, Building and Applying Virtual Humans, Medical and Rehabilitation Applications, Industrial and Ergonomic Applications
Digital Document Processing : Major Directions and Recent Advances
With the advent of the Digital Library initiative, web document processing and biometric aspects of digital document processing, together with new techniques of printed and handwritten Optical Character Recognition (OCR), a good overview of this fast-developing field is invaluable. In this book, all the major and frontier topics in the field of document analysis are brought together into a single volume creating a unique reference source.
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 structure, singularities, and computer vision ; 1st international workshop, DSSCV 2005, Maastricht, The Netherlands, June 9-10, 2005, revised selected papers
Constitutes the refereed post-proceedings of the First International Workshop on Deep Structure, Singularities, and Computer Vision, DSSCV 2005, held in Maastricht. This book represents in understanding the relation between structural, topological information represented by singularities and metric information of signals, shapes, and colors.
Deep Learning to See : Towards New Foundations of Computer Vision
Topics and features: Presents a curiosity-driven approach, posing questions to stimulate readers to design novel computational models of vision Offers a rethinking of computer vision, arguing for an approach based on vision in nature, versus regarding visual signals as collections of images Provides an interdisciplinary commentary, aiming to unify computer vision, machine learning, human vision, and computational neuroscience Serving to inspire and stimulate critical reflection and discussion, yet requiring no prior advanced technical knowledge, the text can naturally be paired with classic textbooks on computer vision to better frame the current state of the art, open problems, and novel potential solutions.
Deep learning architecture and application
As one of the fastest-growing topics in machine learning, deep learning algorithms have achieved unprecedented success in recent years. Novel paradigms (such as contrastive learning and few-shot learning) in deep learning and rising neural network architectures (e.g., transformer and masked autoencoder) are dramatically changing the field of data-driven algorithms. More importantly, deep learning models are redefining the next generation of industrial applications spanning image recognition, speech processing, language translation, healthcare, and other sciences. For example, recent advances in deep representation learning are allowing us to learn about protein 3D structures, which sheds new light on fundamental medicine and biology along with potentially bringing in billions of dollars (e.g., in the pharmaceutical market).
Deep learning and computer vision in remote sensing-I
In the last few years, huge amounts of progress have been made regarding remote sensing in the field of computer vision. This success and progress is mostly due to the effectiveness of deep learning (DL) algorithms. In addition, the remote sensing community has shifted its attention to DL, and DL algorithms have been used to achieve significant success in many image analysis tasks. However, with regard to remote sensing, a number of challenges caused by difficulties in data acquisition and annotation have not been fully solved yet. This reprint is a collection of novel developments in the field of remote sensing using computer vision, deep learning, and artificial intelligence. The articles published involve fundamental theoretical analyses as well as those demonstrating their application to real-world problems.
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.
