skip to main content
10.1145/2482513.2482524acmconferencesArticle/Chapter ViewAbstractPublication Pagesih-n-mmsecConference Proceedingsconference-collections
research-article

SIFT keypoint removal and injection for countering matching-based image forensics

Published: 17 June 2013 Publication History

Abstract

Scale Invariant Feature Transform (SIFT) has been widely employed in several image application domains, including Image Forensics (e.g. detection of copy-move forgery or near duplicates). Until now, the research community has focused on studying the robustness of SIFT against legitimate image processing, but rarely concerned itself with the problem of SIFT security against malicious procedures. Recently, a number of methods allowing to remove SIFT keypoints from an original image have been devised. Although quite effective, such methods produce an attacked image with very few (or no) keypoints, thus leaving cues that can be easily exploited by a forensic analyst to reveal the occurred manipulation. In this paper, we explore the topic of reintroducing fake SIFT keypoints into a previously cleaned image in order to address the main weakness of the existing removal attacks. In particular, we evaluate the fitness of locally adaptive contrast enhancement methods to the task of injecting new keypoints. The results we obtained are encouraging: (i) it is possible to effectively introduce new keypoints whose descriptors do not match with those of the original image, thus concealing the removal forgery; (ii) the perceptual quality of the image following the removal and injection attacks is comparable to the one of the original image.

References

[1]
I. Amerini, L. Ballan, R. Caldelli, A. Del Bimbo, and G. Serra. A sift-based forensic method for copy move attack detection and transformation recovery. Information Forensics and Security, IEEE Transactions on, 6(3):1099 --1110, sept. 2011.
[2]
I. Amerini, M. Barni, R. Caldelli, and A. Costanzo. Counter-forensics of SIFT-based copy-move detection by means of keypoint classification. EURASIP Journal of Image and Video Processing - JIVP, 2013.
[3]
S. Bayram, H. Sencar, and N. Memon. A survey of copy-move forgery detection techniques. In IEEE Western New York Image Processing Workshop, pages 538--542, 2008.
[4]
R. Böhme and M. Kirchner. Counter-forensics: Attacking image forensics. In H. T. Sencar and N. Memon, editors, Digital Image Forensics, pages 327--366. Springer New York, 2013.
[5]
R. Caldelli, I. Amerini, L. Ballan, G. Serra, M. Barni, and A. Costanzo. On the effectiveness of local warping against SIFT-based copy-move detection. In Proc. of Int'l Symposium on Communications, Control and Signal Processing (ISCCSP), Roma, Italy, May 2012.
[6]
T.-T. Do, E. Kijak, L. Amsaleg, and T. Furon. Enlarging hacker's toolbox: deluding image recognition by attacking keypoint orientations. In 37th International Conference on Acoustics, Speech, and Signal Processing, ICASSP'12, Kyoto, Japan, March 2012.
[7]
T.-T. Do, E. Kijak, T. Furon, and L. Amsaleg. Deluding image recognition in sift-based cbir systems. In Proceedings of the 2nd ACM workshop on Multimedia in forensics, security and intelligence, MiFor '10, pages 7--12, 2010.
[8]
T.-T. Do, E. Kijak, T. Furon, and L. Amsaleg. Understanding the security and robustness of sift. In Proceedings of the international conference on Multimedia, MM '10, pages 1195--1198, 2010.
[9]
C.-Y. Hsu, C.-S. Lu, and S.-C. Pei. Secure and robust sift. In Proceedings of the 17th ACM international conference on Multimedia, MM '09, pages 637--640, 2009.
[10]
D. G. Lowe. Distinctive image features from scale-invariant keypoints. Int.'l Journal of Computer Vision, 60(2):91--110, 2004.
[11]
X. Pan and S. Lyu. Region duplication detection using image feature matching. IEEE Transactions on Information Forensics and Security, 5(4):857--867, 2010.
[12]
P. Perona and J. Malik. Scale-space and edge detection using anisotropic diffusion. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 12(7):629--639, 1990.
[13]
G. Schaefer and M. Stich. UCID - An uncompressed colour image database. Storage and Retrieval Methods and Applications for Multimedia, Proceedings of SPIE, 5307:472--480, 2004.
[14]
D. Sheet, H. Garud, A. Suveer, M. Mahadevappa, and J. Chatterjee. Brightness preserving dynamic fuzzy histogram equalization. Consumer Electronics, IEEE Transactions on, 56(4):2475--2480, november 2010.
[15]
A. Vedaldi and B. Fulkerson. VLFeat: An open and portable library of computer vision algorithms. http://www.vlfeat.org/, 2008.
[16]
R. C. Veltkamp and M. Tanase. Content-based image retrieval systems: A survey. Technical report, 2000.
[17]
Z. Wang, A. Bovik, H. Sheikh, and E. Simoncelli. Image quality assessment: From error visibility to structural similarity. Image Processing, IEEE Transactions on, 13(4):600--612, 2004.
[18]
J. Weickert. Coherence-enhancing diffusion filtering. International Journal of Computer Vision, 31(2):111--127, 1999.
[19]
J. Weickert and H. Scharr. A scheme for coherence-enhancing diffusion filtering with optimized rotation invariance. Journal of Visual Communication and Image Representation, 13(1):103--118, 2002.
[20]
D. Zhang and G. Lu. Evaluation of similarity measurement for image retrieval. In Neural Networks and Signal Processing, 2003. Proceedings of the International Conference on, volume 2, pages 928--931. IEEE, 2003.
[21]
K. Zuiderveld. Graphics gems iv. chapter Contrast limited adaptive histogram equalization, pages 474--485. Academic Press Professional, Inc., San Diego, CA, USA, 1994.

Cited By

View all
  • (2023)On the Security of the One-and-a-Half-Class Classifier for SPAM Feature-Based Image ForensicsIEEE Transactions on Information Forensics and Security10.1109/TIFS.2023.326616818(2466-2479)Online publication date: 2023
  • (2018)Forensic Analysis of SIFT Keypoint Removal and InjectionIEEE Transactions on Information Forensics and Security10.1109/TIFS.2014.23376549:9(1450-1464)Online publication date: 25-Dec-2018
  • (2017)SIFT Keypoint Removal via Directed Graph Construction for Color ImagesIEEE Transactions on Information Forensics and Security10.1109/TIFS.2017.273036212:12(2971-2985)Online publication date: Dec-2017
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
IH&MMSec '13: Proceedings of the first ACM workshop on Information hiding and multimedia security
June 2013
242 pages
ISBN:9781450320818
DOI:10.1145/2482513
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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 June 2013

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. counter-forensics
  2. image forensics
  3. keypoint injection
  4. sift keypoints

Qualifiers

  • Research-article

Conference

IH&MMSec '13
Sponsor:

Acceptance Rates

IH&MMSec '13 Paper Acceptance Rate 27 of 74 submissions, 36%;
Overall Acceptance Rate 128 of 318 submissions, 40%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)4
  • Downloads (Last 6 weeks)0
Reflects downloads up to 16 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2023)On the Security of the One-and-a-Half-Class Classifier for SPAM Feature-Based Image ForensicsIEEE Transactions on Information Forensics and Security10.1109/TIFS.2023.326616818(2466-2479)Online publication date: 2023
  • (2018)Forensic Analysis of SIFT Keypoint Removal and InjectionIEEE Transactions on Information Forensics and Security10.1109/TIFS.2014.23376549:9(1450-1464)Online publication date: 25-Dec-2018
  • (2017)SIFT Keypoint Removal via Directed Graph Construction for Color ImagesIEEE Transactions on Information Forensics and Security10.1109/TIFS.2017.273036212:12(2971-2985)Online publication date: Dec-2017
  • (2016)SIFT Keypoint Removal and Injection via Convex RelaxationIEEE Transactions on Information Forensics and Security10.1109/TIFS.2016.255364511:8(1722-1735)Online publication date: Aug-2016
  • (2015)SIFT match removal and keypoint preservation through dominant orientation shift2015 23rd European Signal Processing Conference (EUSIPCO)10.1109/EUSIPCO.2015.7362747(2062-2066)Online publication date: Aug-2015

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media