Sift keypoint removal via convex relaxation
Cheng A.; Li Y.; Zhou J.
2015
Conference NameIEEE International Conference on Multimedia & Expo (ICME)
Source PublicationProceedings - IEEE International Conference on Multimedia and Expo
Volume2015-August
Conference DateJUN 29-JUL 03, 2015
Conference PlaceTurin, ITALY
Abstract

Due to the high robustness against various image transformations, Scale Invariant Feature Transform (SIFT) has been widely employed in many computer vision and multimedia security areas to extract image local features. Though SIFT has been extensively studied from various perspectives, its security against malicious attack has rarely been addressed. In this work, we demonstrate that the SIFT keypoints can be effectively removed, without introducing serious distortion on the image. This is achieved by formulating the SIFT keypoint removal as a constrained optimization problem, where the constraints are well-designed to suppress the existence of local extremum and prevent generating new keypoints within a local cuboid in the scale space. We show that such optimization problem in the ideal case is non-convex. To make the computation feasible, we propose a relaxation technique to convexify the original problem, while maximally preserving the solution space. As demonstrated experimentally, our proposed SIFT removal algorithm significantly outperforms the state-of-the-arts in terms of keypoint removal rate-distortion (KRR-D) performance. Our results imply that an authorization mechanism is required for SIFT-based systems to verify the validity of the input data, so as to achieve high reliability.

KeywordConvex Optimization Convex Relaxation Keypoint Removal Sift
DOI10.1109/ICME.2015.7177423
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Software Engineering ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000380486500046
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Cited Times [WOS]:0   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
AffiliationUniversidade de Macau
Recommended Citation
GB/T 7714
Cheng A.,Li Y.,Zhou J.. Sift keypoint removal via convex relaxation[C],2015.
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