In recent decades, we have witnessed the evolution of information technologies from the development of VLSI technologies, to communication and networking infrastructure, to the standardization of multimedia compression and coding schemes, to effective multimedia content search and retrieval. As a result, multimedia devices and digital content have become ubiquitous. This path of technological evolution has naturally led to a critical issue that must be addressed next, namely, to ensure that content, devices, and intellectual property are being used by authorized users for legitimate purposes, and to be able to forensically prove with high confidence when otherwise. When security is compromised, intellectual rights are violated, or authenticity is forged, forensic methodologies and tools are employed to reconstruct what has happened to digital content in order to answer who has done what, when, where, and how. The goal of this paper is to provide an overview on what has been done over the last decade in the new and emerging field of information forensics regarding theories, methodologies, state-of-the-art techniques, major applications, and to provide an outlook of the future.

http://sig.umd.edu/publications/Stamm_IEEEAccess_201305.pdf

Information Forensics: An Overview of the First Decade

Multicasting is emerging as an enabling technology for multimedia transmissions over wireless networks to support several groups of users with flexible quality of service (QoS) requirements. Although multicast has huge potential to push the limits of next generation communication systems; it is however one of the most challenging issues currently being addressed. In this survey, we explain multicast group formation and various forms of group rate determination approaches. We also provide a systematic review of recent channel-aware multicast scheduling and resource allocation (MSRA) techniques proposed for downlink multicast services in OFDMA based systems. We study these enabling algorithms, evaluate their core characteristics, limitations and classify them using multidimensional matrix. We cohesively review the algorithms in terms of their throughput maximization, fairness considerations, performance complexities, multi-antenna support, optimality and simplifying assumptions. We discuss existing standards employing multicasting and further highlight some potential research opportunities in multicast systems.

Multicast Scheduling and Resource Allocation Algorithms for OFDMA-Based Systems: A Survey

The performance evaluation of video transport mechanisms becomes increasingly important as encoded video accounts for growing portions of the network traffic. Compared to the widely studied MPEG-4 encoded video, the recently adopted H.264 video coding standards include novel mechanisms, such as hierarchical B frame prediction structures and highly efficient quality scalable coding, that have important implications for network transport. This tutorial introduces a trace-based evaluation methodology for the network transport of H.264 encoded video. We first give an overview of H.264 video coding, and then present the trace structures for capturing the characteristics of H.264 encoded video. We give an overview of the typical video traffic and quality characteristics of H.264 encoded video. Finally, we explain how to account for the H.264 specific coding mechanisms, such as hierarchical B frames, in networking studies.

/pdf/video-transport-evaluation-with-h-264-video-traces-2ztswbnb2c.pdf

Video Transport Evaluation With H.264 Video Traces

Previous works on image emotion analysis mainly focused on predicting the dominant emotion category or the average dimension values of an image for affective image classification and regression. However, this is often insufficient in various real-world applications, as the emotions that are evoked in viewers by an image are highly subjective and different. In this paper, we propose to predict the continuous probability distribution of image emotions which are represented in dimensional valence-arousal space. We carried out large-scale statistical analysis on the constructed Image-Emotion-Social-Net dataset, on which we observed that the emotion distribution can be well-modeled by a Gaussian mixture model. This model is estimated by an expectation-maximization algorithm with specified initializations. Then, we extract commonly used emotion features at different levels for each image. Finally, we formalize the emotion distribution prediction task as a shared sparse regression (SSR) problem and extend it to multitask settings, named multitask shared sparse regression (MTSSR), to explore the latent information between different prediction tasks. SSR and MTSSR are optimized by iteratively reweighted least squares. Experiments are conducted on the Image-Emotion-Social-Net dataset with comparisons to three alternative baselines. The quantitative results demonstrate the superiority of the proposed method.

Continuous Probability Distribution Prediction of Image Emotions via Multitask Shared Sparse Regression

Current social networks are of extremely large-scale generating tremendous information flows at every moment. How information diffuses over social networks has attracted much attention from both industry and academics. Most of the existing works on information diffusion analysis are based on machine learning methods focusing on social network structure analysis and empirical data mining. However, the network users' decisions, actions, and socio-economic interactions are generally ignored by most of existing works. In this paper, we propose an evolutionary game theoretic framework to model the dynamic information diffusion process in social networks. Specifically, we derive the information diffusion dynamics in complete networks, uniform degree, and nonuniform degree networks, with the highlight of two special networks, the Erdos-Renyi random network and the Barabasi-Albert scale-free network. We find that the dynamics of information diffusion over these three kinds of networks are scale-free and all the three dynamics are same with each other when the network scale is sufficiently large. To verify our theoretical analysis, we perform simulations for the information diffusion over synthetic networks and real-world Facebook networks. Moreover, we also conduct an experiment on a Twitter hashtags dataset, which shows that the proposed game theoretic model can well fit and predict the information diffusion over real social networks.

Evolutionary Dynamics of Information Diffusion Over Social Networks

Cooperative multicast is an effective technique to provide reliable multicast service in wireless networks. However, selfish nodes may act as free riders to maximize their payoffs, and an incentive mechanism is needed to stimulate cooperation. In this paper, we employ a game theoretic approach to analyze the interactions among selfish mobile nodes in wireless multicast networks. The cooperation process is modeled as an infinite repeated game and the desired cooperation state that achieves absolute fairness and Pareto optimality is derived. A Worst Behavior Tit-for-Tat (WBTFT) incentive strategy is proposed to stimulate cooperation at the desired cooperation state. In the proposed strategy, a node monitors others' behaviors and makes decisions according to the worst behaved node. With perfect monitoring, we analyze the conditions for the proposed strategy to be subgame perfect. To address the issue of imperfect monitoring, an interval based estimation method is proposed and the subgame perfect equilibrium conditions are derived under the assumption that nodes are bounded rational. Simulation results show that the proposed strategy can efficiently enforce cooperation even with imperfect monitoring, and its performance is close to that when all nodes fully cooperate with each other and when they can perfectly monitor each other's behavior without errors.

A Cooperation Stimulation Strategy in Wireless Multicast Networks

In this paper, we consider a multi-user single-relay wireless network, where the relay facilitates transmissions of the users' signals to the destination. We study the relay power allocation among the users, and use bargaining theory to model the negotiation among the users on relay power allocation. By assigning a bargaining power to each user to indicate its transmission priority, we propose an asymmetric Nash bargaining solution (NBS)-based relay power allocation scheme. We also propose a distributed implementation for this solution, where each user only requires its local channel state information (CSI). We analytically investigate the impact of the bargaining powers on the relay power allocation and show that via proper selection of the bargaining powers, the proposed power allocation can achieve a balance between the network sum-rate and the user fairness. Then we generalize the NBS-based power allocation and its distributed implementation to multi-user multi-relay networks. Simulation results are shown to compare the proposed power allocation with sum-rate-optimal power allocation and even power allocation. The impact of the bargaining powers on the power allocation is also demonstrated via simulations.

Power Allocation in Multi-User Wireless Relay Networks through Bargaining

Compressed sensing is a novel technology to acquire and reconstruct signals below the Nyquist rate, and has great potential in image and video acquisition to explore the data redundancy and to significantly reduce the number of sampled data. In this paper, we explore the temporal redundancy in videos, and propose a block-based adaptive framework for compressed video sampling. It addresses the independent movement of different regions in a video, classifies blocks into different types depending on their inter-frame correlation, and adjusts the sampling and reconstruction strategies accordingly. Our framework also considers the diverse texture complexity of different regions, and adaptively adjusts the number of measurements collected for each region based on their sparsity. Our simulation results show that the proposed framework reduces the number of sampled measurements by 52% to 80% while still satisfying the quality constraint on the reconstructed frames. Compared to prior works, our proposed scheme improves the quality of the reconstructed frames and achieves a 0.8dB to 5.4dB gain in the average PSNR.

Block-based adaptive compressed sensing for video

In large-scale media-sharing social networks, where millions of users create, share, link and reuse media content, there are clear challenges in protecting content security and intellectual property, and in designing scalable and reliable networks capable of handling high levels of traffic. This comprehensive resource demonstrates how game theory can be used to model user dynamics and optimize design of media-sharing networks. It reviews the fundamental methodologies used to model and analyze human behavior, using examples from real-world multimedia social networks. With a thorough investigation of the impact of human factors on multimedia system design, this accessible book shows how an understanding of human behavior can be used to improve system performance. Bringing together mathematical tools and engineering concepts with ideas from sociology and human behavior analysis, this one-stop guide will enable researchers to explore this emerging field further and ultimately design media-sharing systems with more efficient, secure and personalized services.

/pdf/behavior-dynamics-in-media-sharing-social-networks-4pjd3asbet.pdf

Behavior Dynamics in Media-Sharing Social Networks

Multimedia social network is a network infrastructure in which the social network users share multimedia contents with all different purposes. Analyzing user behavior in multimedia social networks helps design more secured and efficient multimedia and networking systems. Multimedia fingerprinting protects multimedia from illegal alterations and multiuser collusion is a cost-effective attack. The colluder social network is naturally formed during multiuser collusion with which colluders gain reward by redistributing the colluded multimedia contents. Since the colluders have conflicting interest, the maximal-payoff collusion for one colluder may not be the maximal-payoff collusion for others. Hence, before a collusion being successful, the colluders must bargain with each other to reach agreements. We first model the bargaining behavior among colluders as a noncooperative game and study four different bargaining solutions of this game. Moreover, the market value of the redistributed multimedia content is often time-sensitive. The earlier the colluded copy being released, the more the people are willing to pay for it. Thus, the colluders have to reach agreements on how to distribute reward and risk among themselves as soon as possible. This paper further incorporates this time-sensitiveness of the colluders' reward and studies the time-sensitive bargaining equilibrium. The study in this paper reveals the strategies that are optimal for the colluders; thus, all the colluders have no inventive to disagree. Such understanding reduces the possible types of collusion into a small finite set.

/pdf/game-theoretic-strategies-and-equilibriums-in-multimedia-3h0aua3o3o.pdf

H. Vicky Zhao

Papers

A Cooperation Stimulation Strategy in Wireless Multicast Networks

Power Allocation in Multi-User Wireless Relay Networks through Bargaining

Block-based adaptive compressed sensing for video

Behavior Dynamics in Media-Sharing Social Networks

Game-theoretic strategies and equilibriums in multimedia fingerprinting social networks