Approaching Maximum Embedding Efficiency on Small Covers Using Staircase-Generator Codes
This work addresses the problem of enhancing steganographic efficiency for small data covers, representing a strong specific gain in the domain of information hiding.
The authors tackled the problem of improving embedding efficiency in steganographic matrix embedding by introducing a new family of binary linear codes with a staircase random block structure and an efficient list decoding algorithm. They achieved almost the upper theoretical bound of embedding efficiency for small covers of 1000-1500 bits, which is at least an order of magnitude better than previous results.
We introduce a new family of binary linear codes suitable for steganographic matrix embedding. The main characteristic of the codes is the staircase random block structure of the generator matrix. We propose an efficient list decoding algorithm for the codes that finds a close codeword to a given random word. We provide both theoretical analysis of the performance and stability of the decoding algorithm, as well as practical results. Used for matrix embedding, these codes achieve almost the upper theoretical bound of the embedding efficiency for covers in the range of 1000 - 1500 bits, which is at least an order of magnitude smaller than the values reported in related works.