Vector quantization based scheme for data embedding for images

Today, data hiding has become more and more important in a variety of applications including security. Since Costa's work in the context of communication, the set of quantization based schemes have been proposed as one class of data hiding schemes. Most of these schemes are based on uniform scalar quantizer, which is optimal only if the host signal is uniformly distributed. In this paper, we propose pdf-matched embedding schemes. which not only consider pdf-matched quantizers. but also extend them to multiple dimensions. Specifically, our contributions to this paper are: We propose a pdf-matched embedding (PME) scheme by generalizing the probability distribution of host image and then constructing a pdf-matched quantizer as the starting point. We show experimentally that the proposed pdf-matched quantizer provides better trade-offs between distortion caused by embedding, the robustness to attacks and the embedding capacity. We extend our algorithm to embed a vector of bits in a host signal vector. We show by experiments that our scheme can be closer to the data hiding capacity by embedding larger dimension bit vectors in larger dimension VQs. Two enhancements have been proposed to our method: by vector flipping and by using distortion compensation (DC-PME), that serve to further decrease the embedding distortion. For the I-D case, the PME scheme shows a 1 dB improvement over the QIM method in a robustness-distortion sense, while DC-PME is 1 dB better than DC-QIM and the 4-D vector quantizer based PME scheme performs about 3 dB better than the I-D PME.