Behavior Modeling and Forensics for Multimedia Social Networks

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Within the past decades, the explosive combination of multimedia

signal processing, communications and networking technologies has

facilitated the sharing of digital multimedia data and enabled

pervasive digital media distribution over all kinds of networks.

People involved in the sharing and distribution of multimedia

contents form \emph{multimedia social networks} in which users

share and exchange multimedia content, as well as other resources.

Users in a multimedia social network have different objectives and

influence each other's decision and performance. It is of ample

importance to understand how users interact with and respond to

each other and analyze the impact of human factors on multimedia

systems. This thesis illustrates various aspects of issues and

problems in multimedia social networks via two case studies of

human behavior in multimedia fingerprinting and peer-to-peer live


Since media security and content protection is a major issue in

current multimedia systems, this thesis first studies the user

dynamics of multimedia fingerprinting social networks. We

investigate the side information which improves the

traitor-tracing performance and provide the optimal strategies for

both users (fingerprint detector and the colluders) in the

multimedia fingerprinting social network. Furthermore, before a

collusion being successfully mounted, the colluders must be

stimulated to cooperate with each other and all colluders have to

agree on the attack strategy. Therefore, not all types of

collusion are possible. We reduce the possible collusion set by

analyzing the incentives and bargaining behavior among colluders.

We show that the optimal strategies designed based on human

behavior can provide more information to the fingerprint detector

and effectively improve the collusion resistance.

The second part of this thesis focuses on understanding modelling

and analyzing user dynamics for users in various types of

peer-to-peer live streaming social networks. We stimulate user

cooperation by designing the optimal, cheat-proof, and

attack-resistant strategies for peer-to-peer live streaming social

networks over Internet as well as wireless networks. Also, as more

and more smart-phone users subscribe to the live-streaming

service, a reasonable market price has to be set to prevent the

users from reselling the live video. We start from analyzing the

equilibrium between the users who want to resell the video and the

potential buyers to provide the optimal price for the content