Secure transcoding for compressive multimedia sensing

Li Wei Kang, Chih Yang Lin, Hung Wei Chen, Chia Mu Yu, Chun Shien Lu*, Chao Yung Hsu, Soo Chang Pei

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)


Compressive sensing (CS) has recently attracted much attention due to its unique feature of directly and simultaneously acquiring compressed and encrypted data based on their sparse or compressible properties. To securely transmit compressively sensed multimedia data over networks, it is required to support transcoder to securely convert compressed multimedia into several different types for diverse receivers. In this paper, a secure transcoding scheme for compressive multimedia sensing is proposed. We focus on securely converting compressively sensed multimedia data (not data compressed via standard codec) with a certain number of measurements into other different numbers of measurements without resorting to reconstruct the original data. We show that the security can be achieved via transforming multimedia re-sensing process into another secure domain at the transcoder. We also show that the computational security can be achieved while transmitting compressively sensed data between the sender (or each receiver) and the transcoder over networks.

Original languageEnglish
Title of host publicationICIP 2011
Subtitle of host publication2011 18th IEEE International Conference on Image Processing
Number of pages4
Publication statusPublished - 2011
Externally publishedYes
Event2011 18th IEEE International Conference on Image Processing, ICIP 2011 - Brussels, Belgium
Duration: 2011 Sept 112011 Sept 14

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880


Conference2011 18th IEEE International Conference on Image Processing, ICIP 2011


  • Secure transcoding
  • compressive sensing (CS)
  • multimedia compression and communication
  • sparse representation

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing


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