Enhancing Spatial Steganographic Algorithm Based on Multi-Scale Filters

Steganography is a kind of convert communication technique which uses multimedia carriers such as images, videos, and audios. How to embed secret messages as much as possible under the condition of minimizing the impact on the carrier is always the research focus of steganographic algorithms. After the introduction of STC (syndrome trellis codes), the embedding efficiency of steganographic algorithms can approach the theoretical upper bound. Therefore, the design of steganographic algorithms focuses on the distortion functions which are designed to measure the embedding security of image pixels. Distortion functions are crucial to content-adaptive steganography. For spatial image steganography, the distortion functions are always designed with a texture-complexity criterion of images, where textured regions are assigned low embedding costs and flat regions are assigned high embedding costs. However, for the variety of image contents, this criterion may not be sufficiently satisfied for all pixels in a given image. In this paper, we propose an enhancing spatial steganographic algorithm to refine the embedding costs by using multi-scale filters, which can better enhance texture regions in different scales while reducing the enhancement of smooth regions. The refined embedding costs adequately conform to the above criterion, and thus overcome the problem of improper cost assignment. Experimental results demonstrate that the proposed algorithm can be applied to existing spatial image steganographic algorithms, and can also improve their steganographic security against image steganalysis.