Vol.62, No.3, 2020, pp.1259-1271, doi:10.32604/cmc.2020.06723
An Efficient Steganalysis Model Based on Multi-Scale LTP and Derivative Filters
  • Yuwei Chen1, 2, Yuling Chen1, *, Yu Yang1, 2, Xinda Hao2, Ning Wang2
1 Guizhou University, Guizhou Provincial Key Laboratory of Public Big Data, Guiyang, 550025, China.
2 Information Security Center, Beijing University of Posts and Telecommunications, Beijing, 100876, China.
* Corresponding Author: Yuling Chen. Email: .
Local binary pattern (LBP) is one of the most advanced image classification recognition operators and is commonly used in texture detection area. Research indicates that LBP also has a good application prospect in steganalysis. However, the existing LBP-based steganalysis algorithms are only capable to detect the least significant bit (LSB) and the least significant bit matching (LSBM) algorithms. To solve this problem, this paper proposes a steganalysis model called msdeLTP, which is based on multi-scale local ternary patterns (LTP) and derivative filters. The main characteristics of the msdeLTP are as follows: First, to reduce the interference of image content on features, the msdeLTP uses derivative filters to acquire residual images on which subsequent operations are based. Second, instead of LBP features, LTP features are extracted considering that the LTP feature can exhibit multiple variations in the relationship of adjacent pixels. Third, LTP features with multiple scales and modes are combined to show the relationship of neighbor pixels within different radius and along different directions. Analysis and simulation show that the msdeLTP uses only 2592-dimensional features and has similar detection accuracy as the spatial rich model (SRM) at the same time, showing the high steganalysis efficiency of the method.
Image steganalysis, LTP, multi-scale, image residuals.
Cite This Article
. , "An efficient steganalysis model based on multi-scale ltp and derivative filters," Computers, Materials & Continua, vol. 62, no.3, pp. 1259–1271, 2020.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.