TY - GEN
T1 - Detecting non-aligned double JPEG compression based on refined intensity difference and calibration
AU - Yang, Jianquan
AU - Zhu, Guopu
AU - Wang, Junlong
AU - Shi, Yun Qing
N1 - Funding Information:
The authors would like to thank the anonymous reviewers for their helpful comments. This work has been partially supported by NSFC (61003297, U1135001, 61202415), the NSF of Guangdong Province (S2013010011806), the Shenzhen Peacock Program (KQCX20120816160011790, KQC201109050097A), the Knowledge Innovation Program of Shenzhen (JCYJ20130401170306848), and the 863 Program (2011AA010503).
PY - 2014
Y1 - 2014
N2 - The detection of non-aligned double JPEG (NA-DJPEG) compression is one of the most important topics in the forensics of JPEG image. In this paper, we propose a novel feature set to detect NA-DJPEG compression based on refined intensity difference (RID), a new measure for blocking artifacts. Refined intensity difference is essentially intensity difference with compensation, which takes the negative effect of image texture into consideration when measuring blocking artifacts. The extraction pipeline of the proposed feature set mainly includes two steps. Firstly, two groups of RID histograms (totally sixteen histograms) with respect to horizontal and vertical directions are computed to describe the possible blocking artifacts in each row and column, and the bin values of these histograms are arranged to form an RID feature vector. Then, in order to make the RID feature vector less dependent on image texture and more discriminative, we calibrate it by a reference feature vector to generate a calibrated RID (C-RID) feature vector for final binary classification. Experiments have been conducted to validate the effectiveness of the C-RID feature set, and the results have shown that it outperforms the compared feature sets in most cases.
AB - The detection of non-aligned double JPEG (NA-DJPEG) compression is one of the most important topics in the forensics of JPEG image. In this paper, we propose a novel feature set to detect NA-DJPEG compression based on refined intensity difference (RID), a new measure for blocking artifacts. Refined intensity difference is essentially intensity difference with compensation, which takes the negative effect of image texture into consideration when measuring blocking artifacts. The extraction pipeline of the proposed feature set mainly includes two steps. Firstly, two groups of RID histograms (totally sixteen histograms) with respect to horizontal and vertical directions are computed to describe the possible blocking artifacts in each row and column, and the bin values of these histograms are arranged to form an RID feature vector. Then, in order to make the RID feature vector less dependent on image texture and more discriminative, we calibrate it by a reference feature vector to generate a calibrated RID (C-RID) feature vector for final binary classification. Experiments have been conducted to validate the effectiveness of the C-RID feature set, and the results have shown that it outperforms the compared feature sets in most cases.
KW - Blocking artifacts
KW - Digital forensics
KW - Double compression detection
KW - Non-aligned grid
UR - http://www.scopus.com/inward/record.url?scp=84904758808&partnerID=8YFLogxK
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U2 - 10.1007/978-3-662-43886-2_12
DO - 10.1007/978-3-662-43886-2_12
M3 - Conference contribution
AN - SCOPUS:84904758808
SN - 9783662438855
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 169
EP - 179
BT - Digital-Forensics and Watermarking - 12th International Workshop, IWDW 2013, Revised Selected Papers
PB - Springer Verlag
T2 - 12th International Workshop on Digital-Forensics and Watermarking, IWDW 2013
Y2 - 1 October 2013 through 4 October 2013
ER -