Name
Abstract | ||
|---|---|---|
Synchronization is crucial to design a robust image watermarking scheme. In this paper, a novel feature-based image watermarking scheme against desynchronization attacks is proposed. The robust feature points, which can survive various signal-processing and affine transformation, are extracted by using the Harris-Laplace detector. A local characteristic region (LCR) construction method based on the scale-space representation of an image is considered for watermarking. At each LCR, the digital watermark is repeatedly embedded by modulating the magnitudes of discrete Fourier transform coefficients. In watermark detection, the digital watermark can be recovered by maximum membership criterion. Simulation results show that the proposed scheme is invisible and robust against common signal processing, such as median filtering, sharpening, noise adding, JPEG compression, etc., and desynchronization attacks, such as rotation, scaling, translation, row or column removal, cropping, and random bend attack, etc. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/TIFS.2007.908233 | IEEE Transactions on Information Forensics and Security |
Keywords | Field | DocType |
desynchronization attacks,watermark detection,desynchronization attack,novel feature-based image,jpeg compression,new digital image,robust feature point,digital watermark,algorithm resilient,affine transformation,proposed scheme,robust image,harris-laplace detector,filtering,detectors,watermarking,digital images,transform coding,discrete fourier transform,median filter,feature extraction,scale space,signal processing | Affine transformation,Computer vision,Digital watermarking,Median filter,Pattern recognition,Computer science,Transform coding,Filter (signal processing),Digital image,Watermark,Feature extraction,Artificial intelligence | Journal |
Volume | Issue | ISSN |
2 | 4 | 1556-6013 |
Citations | PageRank | References |
37 | 1.20 | 15 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiang-Yang Wang | 1 | 206 | 20.05 |
| Jun Wu | 2 | 125 | 15.66 |
| Panpan Niu | 3 | 334 | 25.99 |