2007 IEEE International Conference on Image Processing 2007
DOI: 10.1109/icip.2007.4379867
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A Feature-Based Digital Image Watermarking for Copyright Protection and Content Authentication

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Cited by 16 publications
(17 citation statements)
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“…As features of the image have high invariance to distortions, they can be used as a key to find the insertion location. Watermarking algorithms using a feature of an image were proposed as the second generation watermark [7,8,9] Feature detector methods are classified according to their invariance to rotation or similarity or affine and perspective. The goal is to resist both geometric distortion and signal processing attacks.…”
Section: Digital Watermarking Techniquesmentioning
confidence: 99%
See 1 more Smart Citation
“…As features of the image have high invariance to distortions, they can be used as a key to find the insertion location. Watermarking algorithms using a feature of an image were proposed as the second generation watermark [7,8,9] Feature detector methods are classified according to their invariance to rotation or similarity or affine and perspective. The goal is to resist both geometric distortion and signal processing attacks.…”
Section: Digital Watermarking Techniquesmentioning
confidence: 99%
“…Feature extraction method adopted is it allows different degrees of robustness (against distortion) by choosing proper scale parameters. In paper [8], comparison of technique used in [7] and [8] is compared. They suggest that, the feature detector is a key role in the feature-based watermarking.…”
Section: Literature Suggests Different Feature Extraction Techniques Asmentioning
confidence: 99%
“…Neither corner response nor the number of its neighbouring feature points, however, can guarantee the selection of nonoverlapping regions with the maximum robustness to various attacks, because higher corner response and a large number of its neighbouring feature points do not always imply higher robustness of itself. Moreover, a feature region may have different degrees of robustness against different attacks [6]- [7]. These phenomena result in the second issue.…”
Section: Existing Systemsmentioning
confidence: 99%
“…First one is avoiding repeated selection of robust regions to resist similar attacks since the magnitude of pixels in a region will be modified, thus it could be better to select nonoverlapping regions to avoid a major degradation of image quality. For selection of non-overlapping regions here corner as a feature has been used [6]. And the second is selecting the most robust and smallest feature regions set.…”
Section: Introductionmentioning
confidence: 99%
“…For example, the works in [4], [8] matched the original and transformed feature-points for the recovery of the geometric distortions. The blind schemes [5], [9] achieve robustness against geometric transformations without using any information about the original image. Though the semiblind schemes are more robust than their blind counterparts, their applications are limited as the availability of the original information may be impractical.…”
Section: Introductionmentioning
confidence: 99%