The development of steganalysis technology

especially universal blind steganalysis based on statistical characteristics

has increased the demand for steganographic security against statistical analysis. To ensure stego-image security against statistical analysis while avoiding overtraining to an incomplete cover model

this study presents a steganographic Method that minimizes embedding distortion. A novel distortion function reflecting higher order statistics is first defined based on element cliques. According to the Results of theoretical derivation and experiments

the maximal value of the Fisher criterion function is used as the optimization criterion for the parameters in the distortion function

such that the distortion function can be related to statistical detectability. Finally

when a secret message is embedded

multiple different feature subsets are integrated through Gibbs sampling and syndrome-trellis coding.Statistical characteristics are preserved while distortion is minimizes. Experiments are proposed to compare the classification errors of the new Method with those of three similar steganalysis Methods with different dimensions. Results show that the new Method can better preserve image model and maintains high security even when detected using a high-dimensional steganalysis Method. The classification error using corresponding feature set is higher than 0.4 while the embedding rate is 0.5 bit/pixel. The new steganographic Method successfully preserves statistical characteristics while minimizing distortion function. Moreover

the proposed steganographic Method effectively avoids overtraining to an incomplete model and has better adaptability and security than similar Methods.