DDCA: a distortion drift-based cost assignment method for adaptive video steganography in the transform domain

Yi Chen, Hongxia Wang, Kim-Kwang Raymond Choo, Peisong He, Zoran Salcic, Mohamed Ali Kaafar, Xuyun Zhang

Research output: Contribution to journalArticlepeer-review

Abstract

Cost assignment plays a key role in coding performance and security of video steganography. Existing cost assignment methods (for adaptive video steganography) are designed for specific transform coefficients rather than all transform coefficients. In addition, existing video steganographic frameworks do not allow Syndrome-Trellis Codes (STCs) to modify all transform coefficients in both intra-coded and inter-coded frames at the same time. To address these limitations, in this paper, we first propose a novel video steganographic framework. Then, we give a theoretical analysis of distortion drift in both intra- and inter-coding procedures. Based on the analysis, we design a Distortion Drift-based Cost Assignment method, hereafter referred to as DDCA. DDCA considers the inner-block, inter-block and inter-frame distortion costs in order to improve the coding performance and the security of stego videos when the embedding payload is fixed. We conducted extensive experiments using two video datasets to evaluate the proposed video steganographic framework and DDCA, in terms of the coding performance and the security. Our experiments show that the proposed framework outperforms three recent state-of-the-art methods, for example the coding performance and the security of stego videos can benefit from DDCA by making full use of all nonzero transform coefficients.
Original languageEnglish
JournalIEEE Transactions on Dependable and Secure Computing
DOIs
Publication statusE-pub ahead of print - 9 Feb 2021

Keywords

  • Adaptive video steganography
  • Additives
  • Distortion
  • Encoding
  • H264
  • Security
  • Standards
  • Syndrome-Trellis Codes (STCs)
  • Transforms
  • Video coding
  • distortion drift
  • minimal distortion

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