Iterative point matching for registration of free-form curves and surfaces
International Journal of Computer Vision
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
The Hash Function and the Principle of Duality
CGI '01 Computer Graphics International 2001
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
A Metric for Distributions with Applications to Image Databases
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Mathematical Problems in Image Processing: Partial Differential Equations and the Calculus of Variations (Applied Mathematical Sciences)
Local invariant feature detectors: a survey
Foundations and Trends® in Computer Graphics and Vision
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Infinity-norm rotation transforms
IEEE Transactions on Signal Processing
Strengthening digital signatures via randomized hashing
CRYPTO'06 Proceedings of the 26th annual international conference on Advances in Cryptology
Perceptual Image Hashing Via Feature Points: Performance Evaluation and Tradeoffs
IEEE Transactions on Image Processing
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Digital multimedia makes fabricating and copying much easier than ever before. Therefore, it demands efficient and automatic techniques to identify and verify the content of digital multimedia. Image authentication is such a technique to automatically identify whether the query image is a fabrication or a simple copy of the original one. In this paper, we propose a perceptual image authentication technique based on clustering and matching of feature points of images. Feature points are first extracted from images with the k-largest local total variations and clustered using Fuzzy C-means clustering algorithm. Then, feature points in the query image and the anchor image are matched into pairs in zigzag ordering along the diagonals of the images cluster by cluster. In the mean time, the outliers of feature points are removed. Then, the system decisions about the authenticity of images are determined by the majority vote of whether three types of distance between matched feature point pairs are larger than their respective thresholds. The three types of distance include the following: (i) histogram-weighted distance, which is proposed in this paper; (ii) the normalized Euclidean distance; and (iii) the Hausdorff distance. The geometric transform between the query image and the anchor image is estimated, and the query image is registered. The possible tampered image blocks are detected, and the percentage of the tampered area is roughly estimated. The experimental results show the effectiveness and robustness of the proposed image authentication system. Copyright © 2012 John Wiley & Sons, Ltd.