A Spatial Image Steganography Method Based on Nonnegative Matrix Factorization

Abstract

Research on adaptive steganography mainly focuses on how to design a reasonable cost function and how to utilize that cost function to achieve embedding in a stego image with the minimal distortion based on syndrome-trellis codes. Because previous adaptive steganographic methods use convolution with filters to obtain the residuals, these methods do not make good use of the textures of the image itself in the design of the cost function. In this letter, we define a new cost function that uses nonnegative matrix factorization to predict the image pixels and utilizes the mutual dependencies among the pixels to calculate the costs. We present a novel cost function in which the residuals are not calculated via convolution with constant filters. Experimental results show that our method outperforms the state-of-the-art MiPOD, spatial universal wavelet relative distortion, wavelet obtained weights, and HUGO-BD methods in resisting steganalysis based on the spatial rich model and is slightly superior to the high-pass, low-pass, low-pass method.