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Understanding the security and robustness of SIFT

Authors:

Thanh-Toan Do,

Ewa Kijak,

Teddy Furon,

Laurent AmsalegAuthors Info & Claims

MM '10: Proceedings of the 18th ACM international conference on Multimedia

Pages 1195 - 1198

https://doi.org/10.1145/1873951.1874185

Published: 25 October 2010 Publication History

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Abstract

Many content-based retrieval systems (CBIRS) describe images using the SIFT local features because of their very robust recognition capabilities. While SIFT features proved to cope with a wide spectrum of general purpose image distortions, its security has not fully been assessed yet. In one of their scenario, Hsu et al. in [2] show that very specific anti-SIFT attacks can jeopardize the keypoint detection. These attacks can delude systems using SIFT targeting application such as image authentication and (pirated) copy detection.

Having some expertise in CBIRS, we were extremely concerned by their analysis. This paper presents our own investigations on the impact of these anti SIFT attacks on a real CBIRS indexing a large collection of images. The attacks are indeed not able to break the system. A detailed analysis explains this assessment.

References

[1]

T.-T. Do, E. Kijak, T. Furon, and L. Amsaleg. Challenging the security of content based image retrieval systems. In Proc. MMSP, 2010.

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[2]

C.-Y. Hsu, C.-S. Lu, and S.-C. Pei. Secure and robust SIFT. In ACM Multimedia Conf., 2009.

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[3]

J. Law-To, L. Chen, A. Joly, I. Laptev, O. Buisson, V. Gouet-Brunet, N. Boujemaa, and F. Stentiford. Video copy detection: a comparative study. In Proc. CIVR, 2007.

Digital Library

Google Scholar

[4]

H. Lejsek, F. H. Ásmundsson, B. T. Jónsson, and L. Amsaleg. Nv-tree: An efficient disk-based index for approximate search in very large high-dimensional collections. IEEE Trans. Pattern Anal. Mach. Intell., 31(5):869--883, 2009.

Digital Library

Google Scholar

[5]

D. Lowe. Distinctive image features from scale invariant keypoints. IJCV, 60(2), 2004.

Digital Library

Google Scholar

[6]

A. Vedaldi and B. Fulkerson. VLFeat: An open and portable library of computer vision algorithms. http://www.vlfeat.org/, 2008.

Google Scholar

Cited By

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Fan ZLiu ZQu ZQiao FWei QLiu XSun YXu SYang H(2020)ASP-SIFT: Using Analog Signal Processing Architecture to Accelerate Keypoint Detection of SIFT AlgorithmIEEE Transactions on Very Large Scale Integration (VLSI) Systems10.1109/TVLSI.2019.293681828:1(198-211)Online publication date: Jan-2020
https://doi.org/10.1109/TVLSI.2019.2936818
Karunanayake NRajasegaran JGunathillake ASeneviratne SJourjon G(2020)A Multi-modal Neural Embeddings Approach for Detecting Mobile Counterfeit Apps: A Case Study on Google Play StoreIEEE Transactions on Mobile Computing10.1109/TMC.2020.3007260(1-1)Online publication date: 2020
https://doi.org/10.1109/TMC.2020.3007260
Teerakanok SUehara T(2019)A Histogram and GLCM-based Approach for Image Copy-Move Forgery DetectionJournal of Information Processing10.2197/ipsjjip.27.57427(574-584)Online publication date: 2019
https://doi.org/10.2197/ipsjjip.27.574
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Index Terms

Understanding the security and robustness of SIFT
1. Information systems
  1. Information retrieval
    1. Document representation

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Information & Contributors

Information

Published In

MM '10: Proceedings of the 18th ACM international conference on Multimedia

October 2010

1836 pages

ISBN:9781605589336

DOI:10.1145/1873951

General Chairs:
Alberto del Bimbo
University of Florence, Italy
,
Shih-Fu Chang
Columbia University, USA
,
Program Chair:
Arnold Smeulders
University of Amsterdam, NL

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]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 October 2010

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Short-paper

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MM '10

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SIGMM

MM '10: ACM Multimedia Conference

October 25 - 29, 2010

Firenze, Italy

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Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

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Fan ZLiu ZQu ZQiao FWei QLiu XSun YXu SYang H(2020)ASP-SIFT: Using Analog Signal Processing Architecture to Accelerate Keypoint Detection of SIFT AlgorithmIEEE Transactions on Very Large Scale Integration (VLSI) Systems10.1109/TVLSI.2019.293681828:1(198-211)Online publication date: Jan-2020
https://doi.org/10.1109/TVLSI.2019.2936818
Karunanayake NRajasegaran JGunathillake ASeneviratne SJourjon G(2020)A Multi-modal Neural Embeddings Approach for Detecting Mobile Counterfeit Apps: A Case Study on Google Play StoreIEEE Transactions on Mobile Computing10.1109/TMC.2020.3007260(1-1)Online publication date: 2020
https://doi.org/10.1109/TMC.2020.3007260
Teerakanok SUehara T(2019)A Histogram and GLCM-based Approach for Image Copy-Move Forgery DetectionJournal of Information Processing10.2197/ipsjjip.27.57427(574-584)Online publication date: 2019
https://doi.org/10.2197/ipsjjip.27.574
Costanzo AAmerini ICaldelli RBarni M(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
https://dl.acm.org/doi/10.1109/TIFS.2014.2337654
Teerakanok SUehara T(2018)Copy-move Forgery Detection Using GLCM-Based Rotation-Invariant Feature: A Preliminary Research2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC)10.1109/COMPSAC.2018.10259(365-369)Online publication date: Jul-2018
https://doi.org/10.1109/COMPSAC.2018.10259
Li YZhou JCheng A(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
https://doi.org/10.1109/TIFS.2017.2730362
Li YZhou JCheng ALiu XTang Y(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
https://doi.org/10.1109/TIFS.2016.2553645
An Cheng Yuanman Li Jiantao Zhou (2015)Sift keypoint removal via convex relaxation2015 IEEE International Conference on Multimedia and Expo (ICME)10.1109/ICME.2015.7177423(1-6)Online publication date: Jun-2015
https://doi.org/10.1109/ICME.2015.7177423
Amerini IBarni MCaldelli RCostanzo A(2013)Counter-forensics of SIFT-based copy-move detection by means of keypoint classificationEURASIP Journal on Image and Video Processing10.1186/1687-5281-2013-182013:1Online publication date: 15-Apr-2013
https://doi.org/10.1186/1687-5281-2013-18
Amerini IBarni MCaldelli RCostanzo APuech WChaumont MDittmann JCampisi P(2013)SIFT keypoint removal and injection for countering matching-based image forensicsProceedings of the first ACM workshop on Information hiding and multimedia security10.1145/2482513.2482524(123-130)Online publication date: 17-Jun-2013
https://dl.acm.org/doi/10.1145/2482513.2482524
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