Geometrical Attack Robust Spatial Digital Watermarking Based on Improved SIFT

Sun, Jun; Lan, Shishuang

doi:10.1109/cicc-itoe.2010.33

Cited by 6 publications

(1 citation statement)

References 5 publications

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“…A great variety of feature extraction methods has been proposed in the literature. Lately, there is a tendency of using the so-called scale-space methods such as SIFT [20] for watermarking purposes [18,[21][22][23]. In our study, we employed this as well as other feature detectors proposed in the literature, but not in the context of image watermarking, during the past few years.…”

Section: Feature Extractionmentioning

confidence: 99%

Local distortion resistant image watermarking relying on salient feature extraction

Nikolaidis

2012

EURASIP J. Adv. Signal Process.

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The purpose of this article is to present a novel method for region based image watermarking that can tolerate local image distortions to a substantially greater extent than existing methods. The first stage of the method relies on computing a normalized version of the original image using image moments. The next step is to extract a set of feature points that will act as centers of the watermark embedding areas. Four different existing feature extraction techniques are tested: Radial Symmetry Transform (RST), scale-invariant feature transform (SIFT), speeded up robust features (SURF) and features from accelerated segment test (FAST). Instead of embedding the watermark in the DCT domain of the normalized image, we follow the equivalent procedure of first performing the inverse DCT of the original watermark, inversely normalizing it and finally embedding it in the original image. This is done in order to minimize image distortion imposed by inversely normalizing the normalized image to obtain the original. The detection process consists of normalizing the input image and extracting the feature points of the normalized image, after which a correlation detector is employed to detect the possibly inserted watermark in the normalized image. Experimental results demonstrate the relative performance of the four different feature extraction techniques under both geometrical and signal processing operations, as well as the overall superiority of the method against two state-of-the-art techniques that are quite robust as far as local image distortions are concerned.

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Section: Feature Extractionmentioning

confidence: 99%