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Proceedings Paper

Minimizing embedding impact in steganography using trellis-coded quantization
Author(s): Tomáš Filler; Jan Judas; Jessica Fridrich
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Paper Abstract

In this paper, we propose a practical approach to minimizing embedding impact in steganography based on syndrome coding and trellis-coded quantization and contrast its performance with bounds derived from appropriate rate-distortion bounds. We assume that each cover element can be assigned a positive scalar expressing the impact of making an embedding change at that element (single-letter distortion). The problem is to embed a given payload with minimal possible average embedding impact. This task, which can be viewed as a generalization of matrix embedding or writing on wet paper, has been approached using heuristic and suboptimal tools in the past. Here, we propose a fast and very versatile solution to this problem that can theoretically achieve performance arbitrarily close to the bound. It is based on syndrome coding using linear convolutional codes with the optimal binary quantizer implemented using the Viterbi algorithm run in the dual domain. The complexity and memory requirements of the embedding algorithm are linear w.r.t. the number of cover elements. For practitioners, we include detailed algorithms for finding good codes and their implementation. Finally, we report extensive experimental results for a large set of relative payloads and for different distortion profiles, including the wet paper channel.

Paper Details

Date Published: 27 January 2010
PDF: 14 pages
Proc. SPIE 7541, Media Forensics and Security II, 754105 (27 January 2010);
Show Author Affiliations
Tomáš Filler, Binghamton Univ. (United States)
Jan Judas, Binghamton Univ. (United States)
Jessica Fridrich, Binghamton Univ. (United States)

Published in SPIE Proceedings Vol. 7541:
Media Forensics and Security II
Nasir D. Memon; Jana Dittmann; Adnan M. Alattar; Edward J. Delp III, Editor(s)

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