Stegnography in video files using Multivariate Regression and Flexible Macroblock Ordering

Journal Title: IOSR Journals (IOSR Journal of Computer Engineering) - Year 2014, Vol 16, Issue 5

Abstract

Abstract: Data hiding is the ability of embedding data into a digital cover with a minimum amount of perceivable degradation, i.e., the embedded data is invisible or inaudible to a human observer. Data hiding consists of two sets of data, namely the cover medium and the embedding data, which is called the message. In general, there are two types of data hiding for video: one that hides the video content itself (video encryption or scrambling) so that nobody understands what is being transmitted; the other that embeds external information into the video, hence utilizing video as the data host. This paper proposes two data hiding approaches in compressed MPEG video. In the first approach, the quantization scale of a Constant Bit Rate (CBR) video is either incremented or decremented according to the underlying message bit that is to hidden. A second-order multivariate regression is used to associate the macroblock-level features with the hidden message bit. The decoder makes use of this regression model to predict the message bits. However, the message payload is restricted to one bit per macroblock. The second approach of our work is for both constant bit rate and variable bit rate (VBR) coding and it achieves a message payload of three bits per macroblock. The Flexible Macroblock Ordering (FMO) was used to allocate macroblocks to slice groups according to the content of the message bits. In existing network delivery of compressed video, information may be lost if there is the presence of errors or due to attacks. Such losses tend to occur in burst. Thus we can enhance our work to robustness of the existing work against information losses in video steganalysis methods. In this work, we develop an error resilient video encoding approach to help error concealment at the decoder. The existing solutions are very superior in terms of message payload while causing less distortion and compression overhead and the proposed solution reduces the color information losses and thus we can reconstruct the high quality video itself.

Authors and Affiliations

Anila Chandran

Keywords

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  • EP ID EP88945
  • DOI -
  • Views 130
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How To Cite

Anila Chandran (2014).  Stegnography in video files using Multivariate Regression and Flexible Macroblock Ordering. IOSR Journals (IOSR Journal of Computer Engineering), 16(5), 24-28. https://europub.co.uk/articles/-A-88945