Application of a Linear Hash Function in Adaptive Image Steganography

This paper discusses an adaptive method of image steganography issues based on the application of a linear hash function over the GF (2) field to control the embedding process. The method uses staggered splitting of an image into 8 × 8-pixel blocks to provide blind steganography. Classification thresholds are defined as the percentiles of the distribution of gradients throughout the image, allowing for efficient load distribution between textured and smooth areas. Experiments on the BOSSBase, SIPI and Kaggle kits show that the method provides an actual capacity of up to 0.7 bpp at PSNR 47–50 dB and is resistant to statistical tests and RS analysis. At the same time, like other approaches based on modification of pixel differences, it remains vulnerable to modern stegoanalysis based on spatial rich models (SRMs). However, thanks to the modular structure of embedding control based on linear hash function, the proposed architecture allows direct integration with many modern adaptive strategies aimed at minimizing statistical anomalies.