LSB Matching Steganalysis Based on Patterns of Pixel Differences and Random Embedding
This addresses steganalysis for digital image security, but it is incremental as it builds on existing correlation-based methods.
The paper tackles the problem of detecting LSB matching steganography in grayscale images by analyzing patterns of pixel differences before and after random embedding, resulting in better classification accuracy compared to prior art for LSB matching and HUGO steganography, with some applicability to JPEG steganography.
This paper presents a novel method for detection of LSB matching steganogra- phy in grayscale images. This method is based on the analysis of the differences between neighboring pixels before and after random data embedding. In natu- ral images, there is a strong correlation between adjacent pixels. This correla- tion is disturbed by LSB matching generating new types of correlations. The pre- sented method generates patterns from these correlations and analyzes their varia- tion when random data are hidden. The experiments performed for two different image databases show that the method yields better classification accuracy com- pared to prior art for both LSB matching and HUGO steganography. In addition, although the method is designed for the spatial domain, some experiments show its applicability also for detecting JPEG steganography.