A Theory of Multiscale, Curvature-Based Shape Representation for Planar Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
Algorithm 644: A portable package for Bessel functions of a complex argument and nonnegative order
ACM Transactions on Mathematical Software (TOMS)
ISM '06 Proceedings of the Eighth IEEE International Symposium on Multimedia
Adaptive watermark mechanism for rightful ownership protection
Journal of Systems and Software
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Rapid and brief communication: Evolutionary extreme learning machine
Pattern Recognition
Robust lossless image watermarking based on α-trimmed mean algorithm and support vector machine
Journal of Systems and Software
Image analysis by Bessel-Fourier moments
Pattern Recognition
Contourlet-based image watermarking using optimum detector in a noisy environment
IEEE Transactions on Image Processing
OP-ELM: optimally pruned extreme learning machine
IEEE Transactions on Neural Networks
A Constructive and Unifying Framework for Zero-Bit Watermarking
IEEE Transactions on Information Forensics and Security
Adaptive Multiwavelet-Based Watermarking Through JPW Masking
IEEE Transactions on Image Processing
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To overcome some drawbacks existing in current zero-watermarking methods, a lossless copyright authentication scheme is proposed in this paper. This scheme designs a multiple zero-watermarking algorithm based on Bessel-Fourier moment and extreme learning machine (ELM) in curvature-feature domain, develops a method for image feature enhancement and noise suppression in curvature-feature domain, and presents a simple algorithm which uses Bessel-Fourier moment phase to estimate the rotation angle of the rotation-attacked image. The experimental results, involving five types of images, indicate the proposed scheme has better overall performance compared to other five current methods, especially in the aspects of resisting high ratio cropping and large angle rotation attacks. Finally, some related factors including phase and magnitude components, feature vector dimension and ELM optimization are considered in the algorithm performance evaluation.