Robust adaptive steganography based on generalized dither modulation and expanded embedding domain

Sharing images on social network platforms (SNPs) from mobile intelligent devices is becoming more and more popular and has great potential for covert communication. However, images will be processed by lossy social network channels, such as JPEG compression, which reduces image quality and destroys message extraction. Previous robust steganographic schemes using reverse engineering or anti-compression domain for SNPs suffer from some security flaws or have only small capacity and low security level. The purpose of this paper is to refine the robust steganographic scheme by considering asymmetric costs for different modification polarities and expanding the embedding domain for digital images, aiming to aggregate the modifications on the elements with small costs. Such a new strategy that utilizes asymmetric distortion for dither modulation to implement ternary embedding can be regarded as generalized dither modulation in substantial sense. Compared with the original Dither Modulation-based robust Adaptive Steganography (DMAS), the proposed scheme selects more DCT coefficients as cover elements and we call it Generalized dither Modulation-based robust Adaptive Steganography (GMAS). Extensive experiments demonstrate that the proposed GMAS gains significant performance improvements in terms of robustness and security when compared with DMAS. (C) 2019 Elsevier B.V. All rights reserved.