Digital steganography model and embedding optimization strategy

Abstract

The existed digital steganography models and theories are not effective enough to guide the steganography processing. Based on previous studies, this paper proposes a complete digital steganography model based on additive noise. And then, with security analysis from KL divergence, the embedding optimization strategy is given through theoretical derivation needless of any side information: optimizing the embedding modification position and optimizing the embedding modification direction (+1 or − 1). Through theoretical derivation, we also obtain the quantitative relationship between the pixels modification probability and the adjacent pixels difference, and prove that modification by ±1 randomly cannot enhance steganographic security definitely. The research in this paper can provide theoretical guidance for the design of steganography algorithms. Compared with previous studies, the proposed embedding optimization strategy has outstanding advantages of being easy to implement and being effective to improve steganographic security. The experiments by optimizing LSBM and MG algorithms show that the proposed embedding optimization strategy can effectively improve each algorithm’s steganographic security at a relative small payload.