research-article

Seam carving estimation using forensic hash

Authors:

Wenjun Lu,

Min WuAuthors Info & Claims

MM&Sec '11: Proceedings of the thirteenth ACM multimedia workshop on Multimedia and security

Pages 9 - 14

https://doi.org/10.1145/2037252.2037255

Published: 29 September 2011 Publication History

Get Access

Abstract

Seam carving is an adaptive multimedia retargeting technique to resize multimedia data for different display sizes. This technique has found promising applications in media consumption on mobile devices such as tablets and smartphones. However, seam carving can also be used to maliciously alter image content and when combined with other tampering operations, makes tampering detection very difficult by traditional multimedia forensic techniques. In this paper, we study the problem of seam carving estimation and tampering localization using very compact side information called forensic hash. The forensic hash technique bridges two related areas, namely robust image hashing and blind multimedia forensics, to answer a broader scope of forensic questions in a more efficient and accurate manner. We show that our recently proposed forensic hash construction can be extended to accurately estimate seam carving and detect local tampering.

References

[1]

S. Avidan and A. Shamir. Seam carving for content-aware image resizing. ACM Trans. on Graphics, (Proceedings SIGGRAPH 2007), 26(3), 2007.

Digital Library

Google Scholar

[2]

E. Delp, N. Memon, and M. W. (eds). Special issue on forensics analysis of digital evidence. IEEE Signal Processing Magazine, 26(2), March 2009.

Crossref

Google Scholar

[3]

C. Fillion and G. Sharma. Detecting content adaptive scaling of images for forensic applications. In Proc. SPIE: Media Forensics and Security, volume 7541, pages 7541--36, 2010.

Google Scholar

[4]

D. Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2):91{110, 2004.

Digital Library

Google Scholar

[5]

W. Lu, A. Varna, and M. Wu. Forensic hash for multimedia information. In SPIE Media Forensics and Security, 2010.

Google Scholar

[6]

W. Lu and M. Wu. Multimedia forensic hash based on visual words. In Proc. of IEEE ICIP, 2010.

Crossref

Google Scholar

[7]

M. Rubinstein, D. Gutierrez, O. Sorkine, and A. Shamir. A comparative study of image retargeting. ACM Trans. on Graphics, Proceedings Siggraph Asia, 29(5), 2010.

Digital Library

Google Scholar

[8]

M. Rubinstein, D. Gutierrez, O. Sorkine, and A. Shamir. A comparative study of image retargeting. ACM Transactions on Graphics (Proc. SIGGRAPH Asia), 29(5), 2010.

Digital Library

Google Scholar

[9]

A. Sarkar, L. Nataraj, and B. S. Manjunath. Detection of seam carving and localization of seam insertions in digital images. In Proceedings of the 11th ACM workshop on Multimedia and security, pages 107--116, 2009.

Digital Library

Google Scholar

[10]

H. Sencar and N. Memon. Overview of state-of-the-art in digital image forensics. World Scientific Press, 2008.

Crossref

Google Scholar

[11]

A. Swaminathan, Y. Mao, and M. Wu. Robust and secure image hashing. Information Forensics and Security, IEEE Transactions on, 1(2):215--230, 2006.

Digital Library

Google Scholar

Cited By

View all

Gudavalli CRosten ENataraj LChandrasekaran SManjunath B(2022)SeeTheSeams: Localized Detection of Seam Carving based Image Forgery in Satellite Imagery2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)10.1109/CVPRW56347.2022.00010(1-11)Online publication date: Jun-2022
https://doi.org/10.1109/CVPRW56347.2022.00010
Nataraj LGudavalli CManhar Mohammed TChandrasekaran SManjunath B(2021)Seam Carving Detection and Localization Using Two-Stage Deep Neural NetworksMachine Learning, Deep Learning and Computational Intelligence for Wireless Communication10.1007/978-981-16-0289-4_29(381-394)Online publication date: 29-May-2021
https://doi.org/10.1007/978-981-16-0289-4_29
Zhang DChen XLi FSangaiah ADing X(2020)Seam-Carved Image Tampering Detection Based on the Cooccurrence of Adjacent LBPsSecurity and Communication Networks10.1155/2020/88303102020Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.1155/2020/8830310
Show More Cited By

Index Terms

Seam carving estimation using forensic hash
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks

Recommendations

Detection of seam carving and localization of seam insertions in digital images
MM&Sec '09: Proceedings of the 11th ACM workshop on Multimedia and security

"Seam carving" is a recently introduced content aware image resizing algorithm. This method can also be used for image tampering. In this paper, we explore techniques to detect seam carving (or seam insertion) without knowledge of the original image. We ...
Improved seam carving for video retargeting

Video, like images, should support content aware resizing. We present video retargeting using an improved seam carving operator. Instead of removing 1D seams from 2D images we remove 2D seam manifolds from 3D space-time volumes. To achieve this we ...
Detection of seam carving-based video retargeting using forensics hash

Seam carving is a content-aware multimedia retargeting technique to adaptively resize multimedia data for different display sizes. However, it can also be used to remove objects from digital object or video for malicious purposes. In this paper, a ...

Comments

Information & Contributors

Information

Published In

MM&Sec '11: Proceedings of the thirteenth ACM multimedia workshop on Multimedia and security

September 2011

140 pages

ISBN:9781450308069

DOI:10.1145/2037252

General Chair:
Chad Heitzenrater
Air Force Research Lab, USA
,
Program Chairs:
Scott Craver
Binghamton University, USA
,
Jana Dittmann
Otto von Guericke University Magdeburg, Germany

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 September 2011

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

MM&Sec '11

Sponsor:

SIGMM

MM&Sec '11: Multimedia and Security Workshop

September 29 - 30, 2011

New York, Buffalo, USA

Acceptance Rates

Overall Acceptance Rate 128 of 318 submissions, 40%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

21
Total Citations
View Citations
390
Total Downloads

Downloads (Last 12 months)2
Downloads (Last 6 weeks)0

Reflects downloads up to 07 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Gudavalli CRosten ENataraj LChandrasekaran SManjunath B(2022)SeeTheSeams: Localized Detection of Seam Carving based Image Forgery in Satellite Imagery2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)10.1109/CVPRW56347.2022.00010(1-11)Online publication date: Jun-2022
https://doi.org/10.1109/CVPRW56347.2022.00010
Nataraj LGudavalli CManhar Mohammed TChandrasekaran SManjunath B(2021)Seam Carving Detection and Localization Using Two-Stage Deep Neural NetworksMachine Learning, Deep Learning and Computational Intelligence for Wireless Communication10.1007/978-981-16-0289-4_29(381-394)Online publication date: 29-May-2021
https://doi.org/10.1007/978-981-16-0289-4_29
Zhang DChen XLi FSangaiah ADing X(2020)Seam-Carved Image Tampering Detection Based on the Cooccurrence of Adjacent LBPsSecurity and Communication Networks10.1155/2020/88303102020Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.1155/2020/8830310
Cheng HWei JLin CYe J(2020)Detecting Seam-Carved Image by Extreme Learning Machines Using Patch Analysis Method, Jury Voting, and Combinatorial FusionIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2018.278946050:5(1850-1864)Online publication date: May-2020
https://doi.org/10.1109/TSMC.2018.2789460
NazarI HAkgUn D(2020)A Deep learning model for image retargetting level detection2020 4th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)10.1109/ISMSIT50672.2020.9254845(1-4)Online publication date: 22-Oct-2020
https://doi.org/10.1109/ISMSIT50672.2020.9254845
Zhang DWang SWang JSangaiah ALi FSheng V(2019)Detection of Tampering by Image Resizing Using Local Tchebichef MomentsApplied Sciences10.3390/app91530079:15(3007)Online publication date: 26-Jul-2019
https://doi.org/10.3390/app9153007
Lu MNiu S(2019)Detection of Image Seam Carving Using a Novel PatternComputational Intelligence and Neuroscience10.1155/2019/94923582019Online publication date: 11-Nov-2019
https://dl.acm.org/doi/10.1155/2019/9492358
Senturk ZAkgun D(2019)Seam Carving Based Image Retargeting: A Survey2019 1st International Informatics and Software Engineering Conference (UBMYK)10.1109/UBMYK48245.2019.8965618(1-6)Online publication date: Nov-2019
https://doi.org/10.1109/UBMYK48245.2019.8965618
Ye JShi YXu GShi Y(2019)A Convolutional Neural Network Based Seam Carving Detection Scheme for Uncompressed Digital ImagesDigital Forensics and Watermarking10.1007/978-3-030-11389-6_1(3-13)Online publication date: 24-Jan-2019
https://doi.org/10.1007/978-3-030-11389-6_1
Zhang DYang GLi FWang JSangaiah A(2018)Detecting seam carved images using uniform local binary patternsMultimedia Tools and Applications10.1007/s11042-018-6470-y79:13-14(8415-8430)Online publication date: 31-Jul-2018
https://doi.org/10.1007/s11042-018-6470-y
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Abstract

References

Cited By

Index Terms

Recommendations

Detection of seam carving and localization of seam insertions in digital images

Improved seam carving for video retargeting

Detection of seam carving-based video retargeting using forensics hash

Comments

Information

Published In

Sponsors

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Share

Share this Publication link

Share on social media

Affiliations