Preservation and Protection of Online Multimedia Contents

1. Preservation and Protection of Online Multimedia Contents Investigators: AshfaqKhokhar and Rashid Ansari Multimedia Systems Lab. (http://multimedia.ece.uic.edu) Prime Grant Support: National Science Foundation • Emergence of peer to peer networks and increased interest in online sharing poses challenges for preserving and protecting online digital repositories. • Existing efforts are mostly focused on text data. Research challenges are amplified when the contents are multimedia – just re-sampling of voice or image data, which is difficult to detect, compromises the authentication and validation. • Developing multimedia asset management tools and distributed protocols that embed signatures, evaluate authentication, and help perform recovery using copies at peer nodes, if contents have been compromised. • Develop efficient watermarking techniques that can imperceptibly embed information in the media • Embedding capacity (#of bits embedded) of the proposed techniques should be large and embedded information should withstand different types of adversary attacks including re-sampling, compression, noise, desynchronization, etc. – exploit temporal and spatial correlation in the multimedia data. • Develop detection algorithms that can detect the embedded information in the face of modifications and other adversary attacks. • Develop distributed protocols based on trust metrics to recover modified contents • Developed novel watermarking techniques that embed information in selective frequency subbands. The embedded information is 10-15 times more than existing techniques and can withstand adversary attacks. • Developed an Independent Component Analysis based detector that can detect embedded information in the presence of extreme noise (less than 1% error probability even in the presence of 80% noise). • Developing a comprehensive digital asset management system using data hiding for fingerprinting and authentication. • Developing a suite of distributed protocols for content validation and recovery in case of compromised data.