Quality scalability aware watermarking for visual content

Scalable coding-based content adaptation poses serious challenges to traditional watermarking algorithms, which do not consider the scalable coding structure and hence cannot guarantee correct watermark extraction in media consumption chain. In this paper, we propose a novel concept of scalable blind watermarking that ensures more robust watermark extraction at various compression ratios while not effecting the visual quality of host media. The proposed algorithm generates scalable and robust watermarked image code-stream that allows the user to constrain embedding distortion for target content adaptations. The watermarked image code-stream consists of hierarchically nested joint distortion-robustness coding atoms. The code-stream is generated by proposing a new wavelet domain blind watermarking algorithm guided by a quantization based binary tree. The code-stream can be truncated at any distortion-robustness atom to generate the watermarked image with the desired distortion-robustness requirements. A blind extractor is capable of extracting watermark data from the watermarked images. The algorithm is further extended to incorporate a bit-plane discarding-based quantization model used in scalable coding-based content adaptation, e.g., JPEG2000. This improves the robustness against quality scalability of JPEG2000 compression. The simulation results verify the feasibility of the proposed concept, its applications, and its improved robustness against quality scalable content adaptation. Our proposed algorithm also outperforms existing methods showing 35% improvement. In terms of robustness to quality scalable video content adaptation using Motion JPEG2000 and wavelet-based scalable video coding, the proposed method shows major improvement for video watermarking

Recent Trends in Communication Networks

In recent years there has been many developments in communication technology. This has greatly enhanced the computing power of small handheld resource-constrained mobile devices. Different generations of communication technology have evolved. This had led to new research for communication of large volumes of data in different transmission media and the design of different communication protocols. Another direction of research concerns the secure and error-free communication between the sender and receiver despite the risk of the presence of an eavesdropper. For the communication requirement of a huge amount of multimedia streaming data, a lot of research has been carried out in the design of proper overlay networks. The book addresses new research techniques that have evolved to handle these challenges

The multimedia blockchain: a distributed and tamper-proof media transaction framework

A distributed and tamper proof media transaction framework is proposed based on the blockchain model. Current multimedia distribution does not preserve self-retrievable information of transaction trails or content modification histories. For example, digital copies of valuable artworks, creative media and entertainment contents are distributed for various purposes including exhibitions, gallery collections or in media production workflow. Original media is often edited for creative content preparation or tampered with to fabricate false propaganda over social media. However there is no existing trusted mechanism that can easily retrieve either the transaction trails or the modification histories. We propose a novel watermarking based Multimedia Blockchain framework that can address such issues. The unique watermark information contains two pieces of information: a) a cryptographic hash that contains transaction histories (blockchain transactions log) and b) an image hash that preserves retrievable original media content. Once the watermark is extracted, first part of the watermark is passed to a distributed ledger to retrieve the historical transaction trail and the latter part is used to identify the edited / tampered regions. The paper outlines the requirements, the challenges and demonstrates the proof of this concept

Graph Spectral Domain Watermarking for Unstructured Data from Sensor Networks

The modern applications like social networks and sensors networks are increasingly used in the recent years. These applications can be represented as a weighted graph using irregular structure. Unfortunately, we cannot apply the techniques of the traditional signal processing on those graphs. In this paper, graph spread spectrum watermarking is proposed for networked sensor data authentication. Firstly, the graph spectrum is computed based on the eigenvector decomposition of the graph Laplacian. Then, graph Fourier coefficients are obtained by projecting the graph signals onto the basis functions which are the eigenvectors of the graph Laplacian. Finally, the watermark bits are embedded in the graph spectral coefficients using a watermark strength parameter varied according to the eigenvector number. We have considered two scenarios: blind and non-blind watermarking. The experimental results show that the proposed methods are robust, high capacity and result in low distortion in data. The proposed algorithms are robust to many types of attacks: noise, data modification, data deletion, rounding and down-sampling