A Stochastic Approach to Content Adaptive Digital Image Watermarking

Voloshynovskiy, Slava; Herrigel, Alexander; Baumgaertner, Nazanin; Pun, Thierry

doi:10.1007/10719724_16

Cited by 242 publications

(180 citation statements)

References 22 publications

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“…The difference between a pair of images with the same size can be measured by calculating the PSNR (Peak Signal to Noise Ratio) or the Weighted PSNR (WPSNR). The WPSNR is an improved version of the PSNR firstly introduced in [15]. Based on the fact that the human eyes are less sensitive to modifications in textured areas than in smooth areas, the WPSNR uses an additional parameter called the Noise Visibility Function (NVF), which is a texture masking function.…”

Section: Resultsmentioning

confidence: 99%

Robust Resampling Detection in Digital Images

Nguyễn

Katzenbeisser

2012

Communications and Multimedia Security

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Section: Resultsmentioning

confidence: 99%

Robust Resampling Detection in Digital Images

Nguyễn

Katzenbeisser

2012

Communications and Multimedia Security

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“…The first objective metric used was the Weighted Peak Signal-toNoise Ratio (WPSNR) proposed in [22]. Just like PSNR, a high value of WPSNR indicates that the image is less distorted.…”

Section: Visual Quality Of Watermarked Imagesmentioning

confidence: 99%

Perceptual Image Watermarking based on a Mixed-scale Wavelet Representation

Amar¹,

Harba²,

Douzi³

et al. 2017

IJCA

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Perceptual image watermarking consists in using knowledge of the Human Visual System (HVS) to choose the strength of the watermark according to image properties. This paper proposes a new perceptual image watermarking method that combines the advantages of both the wavelet domain and the spatial domain since a mixed-scale wavelet representation is applied. By considering the density of the dominant wavelet coefficients, our scheme is able to differentiate uniform, edge and texture areas. This allowed us to apply adapted luminance, edge or texture masking more efficiently. We selected effective Just Noticeable Difference models from the literature, i.e. luminance and edge masking developed by Chou and Li, and texture masking developed by Qi et al. We also took into account the HVS sensitivity, which varies with the orientation of the image activity. The method was tested on a large database of 50 color images, and compared with four other watermarking methods from the literature. Visual quality tests were conducted and the robustness to attacks was tested compared with four other watermarking methods from the literature. Results show that the proposed method yields a high visual quality and our method is very robust against attacks. This last point is of great importance for real applications. The proposed method is the best trade-off between visual quality and resistance to attacks among the tested methods.

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“…Human visual system should be considered to insert imperceptibly. We apply the perceptual mask of (3) [14]. …”

Section: Watermark Insertionmentioning

confidence: 99%

Geometrically invariant watermarking: synchronization through circular Hough transform

Lee

2007

Multimed Tools Appl

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This paper addresses a geometrically invariant watermarking method for digital images. Most previous watermarking algorithms perform weakly against geometric distortions, which desynchronize the location for the inserted watermark. Watermark synchronization, which is a process for finding the location for watermark insertion and detection, is crucial for robust watermarking. In this paper, we propose a watermarking method that is robust to geometric distortions. In order to synchronize the location for watermark insertion and detection, we use circular Hough transform, which extracts circular features that are invariant to geometric distortions. The circular features are then watermarked using additive way on the spatial domain. Our method belongs to the category of blind watermarking techniques, because we do not need the original image during detection. Experimental results support the contention that our method is useful and considerably robust against both geometric distortion attacks and signal processing attacks as listed in Stirmark 3.1.

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A Stochastic Approach to Content Adaptive Digital Image Watermarking

Cited by 242 publications

References 22 publications

Robust Resampling Detection in Digital Images

Robust Resampling Detection in Digital Images

Perceptual Image Watermarking based on a Mixed-scale Wavelet Representation

Geometrically invariant watermarking: synchronization through circular Hough transform

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