Paper Info
Title
Detecting double compression for JPEG images of low quality factor
Abstract
Double JPEG compression is often used to conceal some illegal operations on the source image. Since there exist some traces when an image undergoes double JPEG compression, the detection of double JPEG compression can be used as a tool of digital forensics. In the past decade, researchers have focused on detecting double compression with the same quality factor (QF) but cannot provide reliable results when the QF is approximately below 70. To remedy this, we propose to analyze the low-frequency components and the error image and extract two types of features to improve the detection accuracy. Since the high-frequency components are easily lost in the JPEG compression, the low-frequency components are utilized to extract the reliable features. With this in mind, our method first analyzes the low-frequency discrete cosine transform coefficients and the lost information. Then, the features are extracted to characterize the difference between the two sequential compressions. Finally, the feature set is fed to a support vector machine for classification. Experimental results on two standard image databases verify that our method could improve the detection accuracy of the low QF double compressed JPEG images with the same QF. The average classification accuracy of the different QFs (the range of QF is usually set from 70 to 20) was obtained as 87.75%. (C) 2019 SPIE and IS&T
Year
DOI
Venue
2019
10.1117/1.JEI.28.3.033011
JOURNAL OF ELECTRONIC IMAGING
Keywords
Field
DocType
digital image forensic,double JPEG compression,low-quality factor
Computer vision,Pattern recognition,Digital forensics,Computer science,Support vector machine,Discrete cosine transform,Double compression,Feature set,JPEG,Artificial intelligence,Jpeg compression
Journal
Volume
Issue
ISSN
28
3
1017-9909
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Huiling Yuan100.34
Bo Ou214510.56
Huawei Tian300.68