Image steganography based on subsampling and compressive sensing

A new image steganography algorithm combining compressive sensing with subsampling is proposed, which can hide secret message into an innovative embedding domain. Considering that natural image tends to be compressible in a transform domain, the characteristics of compressive sensing (CS), dimensional reduction and random projection, are utilized to insert secret message into the compressive sensing transform domain of the sparse image and the measurement matrix which is generated by using a secret key is shared between sender and receiver. Then, stego-image is reconstructed approximately via Total Variation (TV) minimization algorithm. Through adopting different transform coefficients in sub-images gained by subsampling, high perceived quality of the stego-image can be guaranteed. Bit Correction Rate (BCR) between original secret message and extracted message are used to calculate the accuracy of this method. Numerical experiments show that this steganography algorithm has provided a novel data embedding domain and high security of information.