The least significant bit (LSB) matching is a steganographic method which embeds the stego signal into cover images in the spatial domain. However, the stego signal disturbs the correlation of neighboring pixels in cover image and this can be utilized for steganalysis. Local binary pattern (LBP) is an effective image texture descriptor, and it can summarize the correlation of neighboring pixels. In this paper, a LBP-based steganalyzer is proposed to identify the deviations of the correlation violated by the stego noise. Specifically, our paper proposes the local ternary pattern based on path integral (pi-LTP) to enhance the feature discrimination in large-scale pixels. Moreover, a greedy incremental algorithm is utilized in our method to select the optimal subspace of pi-LTP features. Experimental results show our method has a better performance than the state-of-the-art steganalysis methods.
['Qiuyan Lin', 'Jiaying Liu', 'Zongming Guo']
Local ternary pattern based on path integral for steganalysis