An adaptive steganography algorithm based on block sensitivity vectors using HVS features

Steganography is the technique to hide secret data in certain media, digital image for example, without leaving any obvious evidence. As a branch of steganography, HVS feature for years has been one of the issues researchers focus on. In this paper, we introduce an improved adaptive steganography algorithm-SVBA algorithm, which fully analyzes the area statistical properties and adopts HVS features. SVBA algorithm first divides the image into 8*8 blocks and analyzes the mean, variance and entropy value of gray by block, then sets a sensitivity vector for each block with considering HVS features and adjusts the steganography schema dynamically according to the block sensitivity vectors. Simulation experiment results on Matlab7.0 show this algorithm has a balanced performance on efficiency, capacity, imperceptibility and robustness.