Secret image transmission based on VQ and data embedding

Abstract

A new secret image transmission scheme suitable for narrow communication channel is proposed in this article. A set of secret images can be simultaneously and efficiently delivered to the receiver via a small and meaningless data stream by the proposed scheme. To reduce the volume of secret images, a codebook is first generated and these secret images are encoded into binary indexes based on the vector quantization (VQ) technique. The compressed message is then embedded into the VQ codebook utilized in the encoding procedure by an adaptive least-significant-bits (LSB) modification technique. For the purpose of security, the slightly modified codebook is further encrypted into a meaningless data stream by the AES cryptosystem. Simulation results show that the proposed scheme provides an impressive improvement both in the visual quality of the extracted secret images at the receiver and in the hiding capacity of the cover medium. Experimental data also reveal the feasibility of the proposed secret image transmission scheme for limitedbandwidth environment.

Fuzzy declustering-based vector quantization

“…Being analogous to the cell declustering, the fuzzy declustering weights defined in (10) are inversely proportional to the number of data points falling in each fuzzy cluster, and have the following properties:…”

“…Many applications of the VQ idea and its extended versions have been found in image analysis and coding [4][5][6][7][8][9][10]; speech compression, speech and speaker recognition [3,[11][12][13]; and other pattern classification and recognition systems [14][15][16][17][18]. In speech recognition, VQ is typically utilized to reduce the information rate of the speech signal to a much lower rate by the use of a codebook with a relatively small number of codewords; VQ codebook can be used as a recognition processor for efficient speech pattern comparison where the main idea is to use linear predictive coding (LPC) to extract the redundancy from speech signals and subsequently use VQ to approximate a residual signal; and as an effective implementation of discrete hidden Markov models in which both time and spectral constraints are used to quantize an entire speech utterance in a well-defined manner [3].…”

Data hiding using two-layer optimal approach

“…Data hiding methods [1][2][3][4][5][6][7][8][9][10][11] conceal secret data into some kinds of carriers, such as images and videos, for transmitting the data. Invaders are not easy to discover the secret data hidden in the cover carriers.…”