Open Access
ARTICLE
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: .
Computers, Materials & Continua 2020, 62(3), 1259-1271. https://doi.org/10.32604/cmc.2020.06723
Abstract
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.
Keywords
Cite This Article
Y. Chen, Y. Chen, Y. Yang, X. Hao and N. Wang, "An efficient steganalysis model based on multi-scale ltp and derivative filters,"
Computers, Materials & Continua, vol. 62, no.3, pp. 1259–1271, 2020.