A local derivative pattern based image forensic framework for seam carving detection

Jingyu Ye; Yun Qing Shi

doi:10.1007/978-3-319-53465-7_13

A local derivative pattern based image forensic framework for seam carving detection

Jingyu Ye, Yun Qing Shi

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

6 Scopus citations

Abstract

Seam carving is one of the most popular image scaling algorithms which can effectively manipulate the image size while preserving the important image content. In this paper, we present a local derivative pattern (LDP) based forensic framework to detect if a digital image has been processed by seam carving or not. Each image is firstly encoded by applying four LDP encoders. Afterward, 96-D features are extracted from the encoded LDP images, and the support vector machine (SVM) classifier with linear kernel is utilized. The experimental results thus obtained have demonstrated that the proposed framework outperforms the state of the art. Specifically, the proposed scheme has achieved 73%, 88% and 97% average detection accuracies in detecting the low carving rate cases, i.e., 5%, 10% and 20%, respectively; while the prior state-of-the-arts has achieved 66%, 75% and 87% average detection accuracy on these cases.

Original language	English (US)
Title of host publication	Digital Forensics and Watermarking - 15th International Workshop, IWDW 2016, Revised Selected Papers
Editors	Hyoung Joong Kim, Feng Liu, Fernando Perez-Gonzalez, Yun Qing Shi
Publisher	Springer Verlag
Pages	172-184
Number of pages	13
ISBN (Print)	9783319534640
DOIs	https://doi.org/10.1007/978-3-319-53465-7_13
State	Published - 2017
Event	15th International Workshop on Digital-Forensics and Watermarking, IWDW 2016 - Beijing, China Duration: Sep 17 2016 → Sep 19 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10082 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	15th International Workshop on Digital-Forensics and Watermarking, IWDW 2016
Country/Territory	China
City	Beijing
Period	9/17/16 → 9/19/16

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
General Computer Science

Keywords

Image forensics
Local derivative pattern
Seam carving detection
Support vector machine

Access to Document

10.1007/978-3-319-53465-7_13

Cite this

Ye, J., & Shi, Y. Q. (2017). A local derivative pattern based image forensic framework for seam carving detection. In H. J. Kim, F. Liu, F. Perez-Gonzalez, & Y. Q. Shi (Eds.), Digital Forensics and Watermarking - 15th International Workshop, IWDW 2016, Revised Selected Papers (pp. 172-184). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10082 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-53465-7_13

Ye, Jingyu ; Shi, Yun Qing. / A local derivative pattern based image forensic framework for seam carving detection. Digital Forensics and Watermarking - 15th International Workshop, IWDW 2016, Revised Selected Papers. editor / Hyoung Joong Kim ; Feng Liu ; Fernando Perez-Gonzalez ; Yun Qing Shi. Springer Verlag, 2017. pp. 172-184 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{fcc281b3aa1d44779e991c7437892a03,

title = "A local derivative pattern based image forensic framework for seam carving detection",

abstract = "Seam carving is one of the most popular image scaling algorithms which can effectively manipulate the image size while preserving the important image content. In this paper, we present a local derivative pattern (LDP) based forensic framework to detect if a digital image has been processed by seam carving or not. Each image is firstly encoded by applying four LDP encoders. Afterward, 96-D features are extracted from the encoded LDP images, and the support vector machine (SVM) classifier with linear kernel is utilized. The experimental results thus obtained have demonstrated that the proposed framework outperforms the state of the art. Specifically, the proposed scheme has achieved 73%, 88% and 97% average detection accuracies in detecting the low carving rate cases, i.e., 5%, 10% and 20%, respectively; while the prior state-of-the-arts has achieved 66%, 75% and 87% average detection accuracy on these cases.",

keywords = "Image forensics, Local derivative pattern, Seam carving detection, Support vector machine",

author = "Jingyu Ye and Shi, {Yun Qing}",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 15th International Workshop on Digital-Forensics and Watermarking, IWDW 2016 ; Conference date: 17-09-2016 Through 19-09-2016",

year = "2017",

doi = "10.1007/978-3-319-53465-7_13",

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address = "Germany",

}

Ye, J & Shi, YQ 2017, A local derivative pattern based image forensic framework for seam carving detection. in HJ Kim, F Liu, F Perez-Gonzalez & YQ Shi (eds), Digital Forensics and Watermarking - 15th International Workshop, IWDW 2016, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10082 LNCS, Springer Verlag, pp. 172-184, 15th International Workshop on Digital-Forensics and Watermarking, IWDW 2016, Beijing, China, 9/17/16. https://doi.org/10.1007/978-3-319-53465-7_13

A local derivative pattern based image forensic framework for seam carving detection. / Ye, Jingyu; Shi, Yun Qing.
Digital Forensics and Watermarking - 15th International Workshop, IWDW 2016, Revised Selected Papers. ed. / Hyoung Joong Kim; Feng Liu; Fernando Perez-Gonzalez; Yun Qing Shi. Springer Verlag, 2017. p. 172-184 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10082 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - A local derivative pattern based image forensic framework for seam carving detection

AU - Ye, Jingyu

AU - Shi, Yun Qing

N1 - Publisher Copyright: © Springer International Publishing AG 2017.

PY - 2017

Y1 - 2017

N2 - Seam carving is one of the most popular image scaling algorithms which can effectively manipulate the image size while preserving the important image content. In this paper, we present a local derivative pattern (LDP) based forensic framework to detect if a digital image has been processed by seam carving or not. Each image is firstly encoded by applying four LDP encoders. Afterward, 96-D features are extracted from the encoded LDP images, and the support vector machine (SVM) classifier with linear kernel is utilized. The experimental results thus obtained have demonstrated that the proposed framework outperforms the state of the art. Specifically, the proposed scheme has achieved 73%, 88% and 97% average detection accuracies in detecting the low carving rate cases, i.e., 5%, 10% and 20%, respectively; while the prior state-of-the-arts has achieved 66%, 75% and 87% average detection accuracy on these cases.

AB - Seam carving is one of the most popular image scaling algorithms which can effectively manipulate the image size while preserving the important image content. In this paper, we present a local derivative pattern (LDP) based forensic framework to detect if a digital image has been processed by seam carving or not. Each image is firstly encoded by applying four LDP encoders. Afterward, 96-D features are extracted from the encoded LDP images, and the support vector machine (SVM) classifier with linear kernel is utilized. The experimental results thus obtained have demonstrated that the proposed framework outperforms the state of the art. Specifically, the proposed scheme has achieved 73%, 88% and 97% average detection accuracies in detecting the low carving rate cases, i.e., 5%, 10% and 20%, respectively; while the prior state-of-the-arts has achieved 66%, 75% and 87% average detection accuracy on these cases.

KW - Image forensics

KW - Local derivative pattern

KW - Seam carving detection

KW - Support vector machine

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U2 - 10.1007/978-3-319-53465-7_13

DO - 10.1007/978-3-319-53465-7_13

M3 - Conference contribution

AN - SCOPUS:85013499705

SN - 9783319534640

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 172

EP - 184

BT - Digital Forensics and Watermarking - 15th International Workshop, IWDW 2016, Revised Selected Papers

A2 - Kim, Hyoung Joong

A2 - Liu, Feng

A2 - Perez-Gonzalez, Fernando

A2 - Shi, Yun Qing

PB - Springer Verlag

T2 - 15th International Workshop on Digital-Forensics and Watermarking, IWDW 2016

Y2 - 17 September 2016 through 19 September 2016

ER -

Ye J, Shi YQ. A local derivative pattern based image forensic framework for seam carving detection. In Kim HJ, Liu F, Perez-Gonzalez F, Shi YQ, editors, Digital Forensics and Watermarking - 15th International Workshop, IWDW 2016, Revised Selected Papers. Springer Verlag. 2017. p. 172-184. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-53465-7_13

A local derivative pattern based image forensic framework for seam carving detection

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