Open Access
ARTICLE
Multi-Purpose Forensics of Image Manipulations Using Residual- Based Feature
Anjie Peng1, Kang Deng1, Shenghai Luo1, Hui Zeng1, 2, *
1 Southwest University of Science and Technology, Mianyang, 621010, China.
2 Binghamton University, State University of New York, NewYork, 13902, USA.
* Corresponding Author: Hui Zeng. Email: .
Computers, Materials & Continua 2020, 65(3), 2217-2231. https://doi.org/10.32604/cmc.2020.011006
Received 14 April 2020; Accepted 12 June 2020; Issue published 16 September 2020
Abstract
The multi-purpose forensics is an important tool for forge image detection. In
this paper, we propose a universal feature set for the multi-purpose forensics which is
capable of simultaneously identifying several typical image manipulations, including
spatial low-pass Gaussian blurring, median filtering, re-sampling, and JPEG
compression. To eliminate the influences caused by diverse image contents on the
effectiveness and robustness of the feature, a residual group which contains several highpass filtered residuals is introduced. The partial correlation coefficient is exploited from
the residual group to purely measure neighborhood correlations in a linear way. Besides
that, we also combine autoregressive coefficient and transition probability to form the
proposed composite feature which is used to measure how manipulations change the
neighborhood relationships in both linear and non-linear way. After a series of dimension
reductions, the proposed feature set can accelerate the training and testing for the multipurpose forensics. The proposed feature set is then fed into a multi-classifier to train a
multi-purpose detector. Experimental results show that the proposed detector can identify several typical image manipulations, and is superior to the complicated deep CNN-based
methods in terms of detection accuracy and time efficiency for JPEG compressed image
with low resolution.
Keywords
Cite This Article
APA Style
Peng, A., Deng, K., Luo, S., Zeng, H. (2020). Multi-purpose forensics of image manipulations using residual- based feature. Computers, Materials & Continua, 65(3), 2217-2231. https://doi.org/10.32604/cmc.2020.011006
Vancouver Style
Peng A, Deng K, Luo S, Zeng H. Multi-purpose forensics of image manipulations using residual- based feature. Comput Mater Contin. 2020;65(3):2217-2231 https://doi.org/10.32604/cmc.2020.011006
IEEE Style
A. Peng, K. Deng, S. Luo, and H. Zeng "Multi-Purpose Forensics of Image Manipulations Using Residual- Based Feature," Comput. Mater. Contin., vol. 65, no. 3, pp. 2217-2231. 2020. https://doi.org/10.32604/cmc.2020.011006