@Article{cmc.2020.09770,
AUTHOR = {Ming Lu, Shaozhang Niu, Zhenguang Gao},
TITLE = {An Efficient Detection Approach of Content Aware Image Resizing},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {64},
YEAR = {2020},
NUMBER = {2},
PAGES = {887--907},
URL = {http://www.techscience.com/cmc/v64n2/39335},
ISSN = {1546-2226},
ABSTRACT = {Content aware image resizing (CAIR) is an excellent technology used widely 
for image retarget. It can also be used to tamper with images and bring the trust crisis of 
image content to the public. Once an image is processed by CAIR, the correlation of local 
neighborhood pixels will be destructive. Although local binary patterns (LBP) can 
effectively describe the local texture, it however cannot describe the magnitude 
information of local neighborhood pixels and is also vulnerable to noise. Therefore, to 
deal with the detection of CAIR, a novel forensic method based on improved local 
ternary patterns (ILTP) feature and gradient energy feature (GEF) is proposed in this 
paper. Firstly, the adaptive threshold of the original local ternary patterns (LTP) operator 
is improved, and the ILTP operator is used to describe the change of correlation among 
local neighborhood pixels caused by CAIR. Secondly, the histogram features of ILTP and 
the gradient energy features are extracted from the candidate image for CAIR forgery 
detection. Then, the ILTP features and the gradient energy features are concatenated into 
the combined features, and the combined features are used to train classifier. Finally 
support vector machine (SVM) is exploited as a classifier to be trained and tested by the 
above features in order to distinguish whether an image is subjected to CAIR or not. The 
candidate images are extracted from uncompressed color image database (UCID), then 
the training and testing sets are created. The experimental results with many test images 
show that the proposed method can detect CAIR tampering effectively, and that its 
performance is improved compared with other methods. It can achieve a better 
performance than the state-of-the-art approaches.},
DOI = {10.32604/cmc.2020.09770}
}