@Article{cmc.2020.06723,
AUTHOR = {Yuwei Chen, Yuling Chen, Yu Yang, Xinda Hao, Ning Wang},
TITLE = {An Efficient Steganalysis Model Based on Multi-Scale LTP and Derivative Filters},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {62},
YEAR = {2020},
NUMBER = {3},
PAGES = {1259--1271},
URL = {http://www.techscience.com/cmc/v62n3/38353},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2020.06723}
}