Abstract:

Steganography is an information hiding method.As people increasingly favor social media for information exchange,information hiding technology with images is a hot topic of current research.In the framework of minimizing embedding distortion,it is crucial to define a good cost function in order to improve the security of steganography.In this paper we propose a distortion scheme for spatial steganography,in which the Hessian matrix is constructed via the second order difference and is used for calculating the residual.First,we use the horizontal difference and vertical difference,and directional differences in 4 directions,to calculate Hessian matrices in 4 directions.For these Hessian matrices,we calculate absolute values of their eigenvalues as a residual.Along with cost values calculated from residuals,we merge them in two phases.In the experiment,we use two kinds of steganalysis feature sets,SRM and MaxSRMd2,respectively,to evaluate the performance of each distortion scheme.Moreover,an ensemble FLD classifier is adopted to map the steganalysis feature to the prediction results for each testing sample.The experimental result is compared to those of eight methods and each method is repeated ten times.Experimental data show that using the distortion cost scheme proposed in this paper exhibits a higher sample detection error rate at the same embedding rate.Experimental results show that the proposed method achieves a security performance comparable to that by the Hill.In addition,when our proposed method is fused with the Hill by the Controversial Pixel Prior (CPP) rule,its performance can be enhanced.

Key words: steganography, steganalysis, additive distortion, cost function, distortion function