An Approach to Optimal Watermark Detection

Stanković, Srdjan; Djurović, Igor; Herpers, Rainer; Stanković, Ljubiša

doi:10.1078/1434-8411-54100185

Cited by 19 publications

(25 citation statements)

References 8 publications

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“…The DCT coefficients are also reordered into a zig-zag sequence, and the first DCT coefficient, i.e. the DC term, should be not modified [1][2][3][4][5][6][7][8]. The coefficients from the (L + 1)th to the (L + 100M 2 )th positions are taken out according to the above zig-zag sequence of the DCT spectrum, as shown in Fig.…”

Section: Watermark Castingmentioning

confidence: 99%

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The Application of Saturating Detectors to a DCT-Domain Watermarking Scheme

2008

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The present paper proposes a discrete cosine transform (DCT) domain watermarking scheme by exploiting nonlinear dynamical saturating detectors in the design of a watermark detection process. A binary copyright character, i.e. watermark to be hidden into an image, is firstly reordered into a binary zig-zag sequence, and then mapped onto the pulse amplitude modulated signal. A certain desynchronization time delay can be deliberately placed into one code of the modulated signal, and is tolerated due to the superior robustness of nonlinear detectors over matched filters. A selected set of DCT coefficients of a host image in a mid frequency range is shuffled by the Arnold transform, which makes it look more like background noise with respect to the watermark signal. Then, the watermark signal is embedded in the set of shuffled DCT coefficients. The copyright character can be extracted by a nonlinear saturating detector without prior knowledge of the original image and watermark, i.e. blind watermark detection. Interestingly, a higher match between the original watermark character and the extracted one can be further achieved by a parallel array of nonlinear detectors via the mechanism of array stochastic resonance. Robustness of the proposed watermarking scheme is shown in the presence of noise, filtering, cropping, and compression.

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Section: Watermark Castingmentioning

confidence: 99%

“…Digital watermarking has recently received great attention as a tool for copyright protection [1][2][3][4][5][6][7][8][9][10]. A digital watermark contains information about the copyright owner, the authorized consumer or other information that is desired to be embedded.…”

Section: Introductionmentioning

confidence: 99%

The Application of Saturating Detectors to a DCT-Domain Watermarking Scheme

2008

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“…The digital watermarking provides data protection [9], [10]. In the digital watermarking, the embedded algorithm (encoder) inserts any information or data to the measurement matrix.…”

Section: Introductionmentioning

confidence: 99%

Watermarked Compressive Sensing Measurements Reconstructed by the Greedy Algorithms

Bash¹,

Kayhan²

2015

IJCTE

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Abstract-This paper proposes a CS based watermarking algorithm. In this algorithm, the watermarking image embeds into the compressive measurement vectors by the encoder. The measurement vectors are sparse in suitable basis. The resulting watermarked measurements recover by using both the OMP and the OMP-PKS reconstruction algorithms. Then the decoder uses to extract the watermarking image.In an experimental study, results obtained by the OMP and the OMP-PKS algorithms are compared. The results show that the OMP-PKS algorithm achieves performance superior to that of the OMP reconstruction algorithm.

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“…For these non-Gaussian probability density functions (pdf), a locally optimum detector is usually derived for watermark detection [17,19,20,[26][27][28][29]. Especially, Barni et al noted that the proposed watermark embedding rule in [16,18] does not obey an additive rule, and presented a new decoder for the optimum recovery of watermarks [17].…”

Section: Introductionmentioning

confidence: 99%

“…So far, there are mainly two main proposed watermarking techniques, i.e., embedding the watermark in the spatial domain [1, 5-7, 12, 13] or in the transformed domain [1,6,11,16,18,26,27] of a host image. It has been shown that the discrete cosine transformation (DCT) coefficients of images have interesting properties that can be exploited for watermark detection [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Sign Detector for DCT Domain Watermark Detection

2011

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Reordering by the rule of decreased absolute amplitudes, the discrete cosine transformation (DCT) coefficients of an image are approximately modeled as dichotomous noise. Based on this assumption, it is interesting to note that the classical multiplicative embedding method can be transformed into an additive embedding rule, which accords with the signal processing problem of detecting a known weak signal in additive non-Gaussian noise. Then, following the generalized Neyman-Pearson lemma, a locally optimum detector, named the sign detector, is introduced to distinguish the correct watermark from the wrong ones. The statistical characteristics of this nonlinear sign detector are analytically investigated in detail. Extensive experimental results demonstrate the robustness of watermark against some common attacks, e.g., JPEG compression, cropping, filtering, additive Gaussian noise, dithering, and also verify the robust performance of the nonlinear sign detector for watermark detection.

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An Approach to Optimal Watermark Detection

Cited by 19 publications

References 8 publications

The Application of Saturating Detectors to a DCT-Domain Watermarking Scheme

The Application of Saturating Detectors to a DCT-Domain Watermarking Scheme

Watermarked Compressive Sensing Measurements Reconstructed by the Greedy Algorithms

Evaluation of the Sign Detector for DCT Domain Watermark Detection

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