LAUSR

Existing cover selection methods for steganography mainly focus on embedding distortion of each image, but ignore the similarity between images. When the cover images are similar, a number of relevant samples are provided to steganalysis, which is disadvantageous to steganography. This paper proposes a new cover selection method to joint image similarity and embedding distortion. Due to the difference between steganography and other image processing tasks, e.g., image reconstruction, image recognition, we propose a customized method to calculate image similarity for steganography based on SVD (singular value decomposition). Importantly, the small singular values (instead of the large ones) are employed, since it is suitable for the properties of steganography. In addition, embedding distortion is calculated by the current distortion minimization framework. The obtained image similarity and embedding distortion are combined to form a new cover selection strategy. As a result, the properties of batch images can be fully used. Experimental results show that our scheme outperforms the state-of-the-art cover selection methods when they are checked by modern steganalytic tools.