Secure Binary Image Steganography With Distortion Measurement Based on Prediction

Abstract

In this paper, a binary image steganographic scheme is presented, which aims at minimizing the embedding distortions measured by prediction. A prediction model of the center pixel’s value is established in a $3 \times 3$ local region. A concept of “uncertainty” is introduced to represent the prediction result and the uncertainty is defined as the proximity of probabilities about whether the center pixel is black or white. A pixel with high uncertainty means that it is hard to distinguish whether it has been flipped or not, and thus the distortion introduced by flipping this pixel is small. The uncertainty is an appended statistical explanation of human visual perception and the distortion measurement based on it can evaluate the embedding changes on both vision and statistics. Benefiting from the statistics, uncertainty can evaluate the distortion influence in an extended local region. To play the advantage of distortion measurement, the syndrome-trellis code (STC) is employed to minimize the embedding distortions. Comparisons with prior schemes demonstrate that the proposed steganographic scheme achieves high vision imperceptibility and statistical security.