A Video Steganography Method based on Transform Block Decision for H.265/HEVC

High definition video application has drawn a lot of interest both from academy and industry. The relevant latest video coding technology, H.265/HEVC has been a promising area for video steganography. In this paper, we present a novel and efficient video steganography method based on transform block decision for H.265. In order to improve the visual quality of carrier video, we analyze the embedding error of data hiding with modifying partitioning parameters of CB, PB and TB, and modify the transform block decision to embed secret message and update corresponding residuals synchronously. In order to limit embedding error, we utilize an efficient embedding mapping rule which can embed N (N>1) bits message and at most modify one bit transform partitioning flag. Our experimental results show that the proposed method can achieve better visual quality, larger embedding capacity and less bit-rate increase than state-of-the-art researches

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

Cryptography-based secure data storage and sharing using HEVC and public clouds

© 2016 Elsevier Inc. Mobile devices are widely used for uploading/downloading media files such as audio, video and images to/from the remote servers. These devices have limited resources and are required to offload resource-consuming media processing tasks to the clouds for further processing. Migration of these tasks means that the media services provided by the clouds need to be authentic and trusted by the mobile users. The existing schemes for secure exchange of media files between the mobile devices and the clouds have limitations in terms of memory support, processing load, battery power, and data size. These schemes lack the support for large-sized video files and are not suitable for resource-constrained mobile devices. This paper proposes a secure, lightweight, robust, and efficient scheme for data exchange between the mobile users and the media clouds. The proposed scheme considers High Efficiency Video Coding (HEVC) Intra-encoded video streams in unsliced mode as a source for data hiding. Our proposed scheme aims to support real-time processing with power-saving constraint in mind. Advanced Encryption Standard (AES) is used as a base encryption technique by our proposed scheme. The simulation results clearly show that the proposed scheme outperforms AES-256 by decreasing the processing time up to 4.76% and increasing the data size up to 0.72% approximately. The proposed scheme can readily be applied to real-time cloud media streaming

Efficient HEVC-based video adaptation using transcoding

In a video transmission system, it is important to take into account the great diversity of the network/end-user constraints. On the one hand, video content is typically streamed over a network that is characterized by different bandwidth capacities. In many cases, the bandwidth is insufficient to transfer the video at its original quality. On the other hand, a single video is often played by multiple devices like PCs, laptops, and cell phones. Obviously, a single video would not satisfy their different constraints. These diversities of the network and devices capacity lead to the need for video adaptation techniques, e.g., a reduction of the bit rate or spatial resolution. Video transcoding, which modifies a property of the video without the change of the coding format, has been well-known as an efficient adaptation solution. However, this approach comes along with a high computational complexity, resulting in huge energy consumption in the network and possibly network latency. This presentation provides several optimization strategies for the transcoding process of HEVC (the latest High Efficiency Video Coding standard) video streams. First, the computational complexity of a bit rate transcoder (transrater) is reduced. We proposed several techniques to speed-up the encoder of a transrater, notably a machine-learning-based approach and a novel coding-mode evaluation strategy have been proposed. Moreover, the motion estimation process of the encoder has been optimized with the use of decision theory and the proposed fast search patterns. Second, the issues and challenges of a spatial transcoder have been solved by using machine-learning algorithms. Thanks to their great performance, the proposed techniques are expected to significantly help HEVC gain popularity in a wide range of modern multimedia applications

Entropy in Image Analysis II

Image analysis is a fundamental task for any application where extracting information from images is required. The analysis requires highly sophisticated numerical and analytical methods, particularly for those applications in medicine, security, and other fields where the results of the processing consist of data of vital importance. This fact is evident from all the articles composing the Special Issue "Entropy in Image Analysis II", in which the authors used widely tested methods to verify their results. In the process of reading the present volume, the reader will appreciate the richness of their methods and applications, in particular for medical imaging and image security, and a remarkable cross-fertilization among the proposed research areas

Error resilience and concealment techniques for high-efficiency video coding

This thesis investigates the problem of robust coding and error concealment in High Efficiency Video Coding (HEVC). After a review of the current state of the art, a simulation study about error robustness, revealed that the HEVC has weak protection against network losses with significant impact on video quality degradation. Based on this evidence, the first contribution of this work is a new method to reduce the temporal dependencies between motion vectors, by improving the decoded video quality without compromising the compression efficiency. The second contribution of this thesis is a two-stage approach for reducing the mismatch of temporal predictions in case of video streams received with errors or lost data. At the encoding stage, the reference pictures are dynamically distributed based on a constrained Lagrangian rate-distortion optimization to reduce the number of predictions from a single reference. At the streaming stage, a prioritization algorithm, based on spatial dependencies, selects a reduced set of motion vectors to be transmitted, as side information, to reduce mismatched motion predictions at the decoder. The problem of error concealment-aware video coding is also investigated to enhance the overall error robustness. A new approach based on scalable coding and optimally error concealment selection is proposed, where the optimal error concealment modes are found by simulating transmission losses, followed by a saliency-weighted optimisation. Moreover, recovery residual information is encoded using a rate-controlled enhancement layer. Both are transmitted to the decoder to be used in case of data loss. Finally, an adaptive error resilience scheme is proposed to dynamically predict the video stream that achieves the highest decoded quality for a particular loss case. A neural network selects among the various video streams, encoded with different levels of compression efficiency and error protection, based on information from the video signal, the coded stream and the transmission network. Overall, the new robust video coding methods investigated in this thesis yield consistent quality gains in comparison with other existing methods and also the ones implemented in the HEVC reference software. Furthermore, the trade-off between coding efficiency and error robustness is also better in the proposed methods