Optimal steganography with blind detection based on Bayesian optimization algorithm

An optimal steganography method is provided to embed the secret data into the low-order bits of host pixels. The main idea of the proposed method is that before the embedding process, the secret data are mapped to the optimal values using Bayesian optimization algorithm (along with introducing a novel mutation operator), in order to reduce the mean square error (MSE) and also maintain the structural similarity between the images before and after embedding (i.e., preserving the visual quality of the embedded-image). Then, the mapped data are embedded into the low-order bits of host pixels using modulus function and a systematic and reversible algorithm. Since the proposed method is able to embed data into more significant bits, it has enhanced the payload, while preserving the visual quality of the image. Extraction of data from the host image is possible without requiring the original image. The simulation results show that the proposed algorithm can lead to a minimum loss in MSE criterion and also a minimal reduction in visual quality of the image in terms of diagnostic criteria of the human eye, whereas there is no limitation on the improvement of payload, in comparison with other methods.