Open Access
ARTICLE
An Efficient Detection Approach of Content Aware Image Resizing
Ming Lu1, 2, *, Shaozhang Niu1, Zhenguang Gao3
1 Beijing Key Lab of Intelligent Telecommunication Software and Multimedia, Beijing University of Posts
and Telecommunications, Beijing, 100876, China.
2 School of Computer Science and Software Engineering, University of Science and Technology Liaoning,
Anshan, 114051, China.
3 Department of Computer Science, Framingham State University, Massachusetts, MA 01772, USA.
* Corresponding Author: Ming Lu. Email: .
Computers, Materials & Continua 2020, 64(2), 887-907. https://doi.org/10.32604/cmc.2020.09770
Received 18 January 2020; Accepted 30 April 2020; Issue published 10 June 2020
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.
Keywords
Cite This Article
M. Lu, S. Niu and Z. Gao, "An efficient detection approach of content aware image resizing,"
Computers, Materials & Continua, vol. 64, no.2, pp. 887–907, 2020.
Citations