Defining Cost Functions for Adaptive JPEG Steganography at the Microscale

Abstract

Minimal distortion steganography is the most successful model for adaptive steganography, in which the cost function determines the security. Texture complexity is the major factor in defining cost function in images. In this paper, we proposed a method to improve the cost function of JPEG steganography by exploiting the texture in microscale. The proposed scheme is designed by using a “microscope” to highlight details in an image, so that distortion definition can be more refined. Linear unsharp masking acts as the microscope, because it can accentuate the texture region as well as maintain the original characteristics of images. Inter-block spreading rule is proposed to further strengthen the security. We improve the state-of-the-art schemes, J-UNIWARD and UERD, as J-UNIWARD has outstanding performance on resisting detection while UERD has significant lower computational complexity. In order to keep high efficiency of UERD, filtering in the DCT domain is introduced. Extending experiments show that in most cases the proposed methods (J-MSUNIWARD and MSUERD) can achieve a higher level of security than the original methods.