@Article{cmc.2020.011006,
AUTHOR = {Anjie Peng, Kang Deng, Shenghai Luo, Hui Zeng},
TITLE = {Multi-Purpose Forensics of Image Manipulations Using Residual- Based Feature},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {65},
YEAR = {2020},
NUMBER = {3},
PAGES = {2217--2231},
URL = {http://www.techscience.com/cmc/v65n3/40165},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2020.011006}
}