Deep Learning Methods for Efficient Image Coding

Video data makes up 58% of all internet traffic and is growing as self-driving car cameras, 4K televisions, and video surveillance systems continue to come online. Traditional heuristics based image and video codecs such as JPEG and HEVC have been successful thus far, however, these approaches lack the ability to leverage big data to gain massive insights. Six deep learning based approaches are proposed to tackle efficient image/video compression and image compression for machine classification

A Comprehensive Review of Video Steganalysis

Steganography is the art of secret communication and steganalysis is the art of detecting the hidden messages embedded in digital media covers. One of the covers that is gaining interest in the field is video. Presently, the global IP video traffic forms the major part of all consumer Internet traffic. It is also gaining attention in the field of digital forensics and homeland security in which threats of covert communications hold serious consequences. Thus, steganography technicians will prefer video to other types of covers like audio files, still images or texts. Moreover, video steganography will be of more interest because it provides more concealing capacity. Contrariwise, investigation in video steganalysis methods does not seem to follow the momentum even if law enforcement agencies and governments around the world support and encourage investigation in this field. In this paper, we review the most important methods used so far in video steganalysis and sketch the future trends. To the best of our knowledge this is the most comprehensive review of video steganalysis produced so far

深層学習に基づく画像圧縮と品質評価

早大学位記番号:新8427早稲田大

Beyond the pixels: learning and utilising video compression features for localisation of digital tampering.

Video compression is pervasive in digital society. With rising usage of deep convolutional neural networks (CNNs) in the fields of computer vision, video analysis and video tampering detection, it is important to investigate how patterns invisible to human eyes may be influencing modern computer vision techniques and how they can be used advantageously. This work thoroughly explores how video compression influences accuracy of CNNs and shows how optimal performance is achieved when compression levels in the training set closely match those of the test set. A novel method is then developed, using CNNs, to derive compression features directly from the pixels of video frames. It is then shown that these features can be readily used to detect inauthentic video content with good accuracy across multiple different video tampering techniques. Moreover, the ability to explain these features allows predictions to be made about their effectiveness against future tampering methods. The problem is motivated with a novel investigation into recent video manipulation methods, which shows that there is a consistent drive to produce convincing, photorealistic, manipulated or synthetic video. Humans, blind to the presence of video tampering, are also blind to the type of tampering. New detection techniques are required and, in order to compensate for human limitations, they should be broadly applicable to multiple tampering types. This thesis details the steps necessary to develop and evaluate such techniques

Super-Resolution in Still Images and Videos via Deep Learning

PhDThe evolution of multimedia systems and technology in the past decade has enabled production and delivery of visual content in high resolution, and the thirst for achieving higher de nition pictures with more detailed visual characteristics continues. This brings attention to a critical computer vision task for spatial up-sampling of still images and videos called super-resolution. Recent advances in machine learning, and application of deep neural networks, have resulted in major improvements in various computer vision applications. Super-resolution is not an exception, and it is amongst the popular topics that have been a ected signi cantly by the emergence of deep learning. Employing modern machine learning solutions has made it easier to perform super-resolution in both images and videos, and has allowed professionals from di erent elds to upgrade low resolution content to higher resolutions with visually appealing picture delity. In spite of that, there remain many challenges to overcome in adopting deep learning concepts for designing e cient super-resolution models. In this thesis, the current trends in super-resolution, as well as the state of the art are presented. Moreover, several contributions for improving the performance of the deep learning-based super-resolution models are described in detail. The contributions include devising theoretical approaches, as well as proposing design choices that can lead to enhancing the existing art in super-resolution. In particular, an e ective approach for training convolutional networks is proposed, that can result in optimized and quick training of complex models. In addition, speci c deep learning architectures with novel elements are introduced that can provide reduction in the complexity of the existing solutions, and improve the super-resolution models to achieve better picture quality. Furthermore, application of super-resolution for handling compressed content, and its functionality as a compression tool are studied and investigated.COGNITUS media AI software fundin

Super-resolution assessment and detection

Super Resolution (SR) techniques are powerful digital manipulation tools that have significantly impacted various industries due to their ability to enhance the resolution of lower quality images and videos. Yet, the real-world adaptation of SR models poses numerous challenges, which blind SR models aim to overcome by emulating complex real-world degradations. In this thesis, we investigate these SR techniques, with a particular focus on comparing the performance of blind models to their non-blind counterparts under various conditions. Despite recent progress, the proliferation of SR techniques raises concerns about their potential misuse. These methods can easily manipulate real digital content and create misrepresentations, which highlights the need for robust SR detection mechanisms. In our study, we analyze the limitations of current SR detection techniques and propose a new detection system that exhibits higher performance in discerning real and upscaled videos. Moreover, we conduct several experiments to gain insights into the strengths and weaknesses of the detection models, providing a better understanding of their behavior and limitations. Particularly, we target 4K videos, which are rapidly becoming the standard resolution in various fields such as streaming services, gaming, and content creation. As part of our research, we have created and utilized a unique dataset in 4K resolution, specifically designed to facilitate the investigation of SR techniques and their detection