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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: email.

Computers, Materials & Continua 2020, 64(2), 887-907. https://doi.org/10.32604/cmc.2020.09770

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.

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Cite This Article

APA Style
Lu, M., Niu, S., Gao, Z. (2020). An efficient detection approach of content aware image resizing. Computers, Materials & Continua, 64(2), 887-907. https://doi.org/10.32604/cmc.2020.09770
Vancouver Style
Lu M, Niu S, Gao Z. An efficient detection approach of content aware image resizing. Comput Mater Contin. 2020;64(2):887-907 https://doi.org/10.32604/cmc.2020.09770
IEEE Style
M. Lu, S. Niu, and Z. Gao "An Efficient Detection Approach of Content Aware Image Resizing," Comput. Mater. Contin., vol. 64, no. 2, pp. 887-907. 2020. https://doi.org/10.32604/cmc.2020.09770

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cc 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.
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