Lightweight Detection of Additive Watermarking in the DWT-Domain

Kwitt, Roland; Meerwald, Peter; Uhl, Andreas

doi:10.1109/tip.2010.2064327

Cited by 54 publications

(35 citation statements)

References 27 publications

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“…Our own experiments showed that GGD and Cauchy model parameters are hard to reliably estimate on heavily quantized data (e.g. H.264 4 × 4 DCT residuals) and that the corresponding LRT detectors do not achieve optimal performance even employing ML estimates [10].…”

Section: Application Considerationsmentioning

confidence: 99%

Watermark detection on quantized transform coefficients using product bernoulli distributions

Meerwald

Uhl

2010

Proceedings of the 12th ACM Workshop on Multimedia and Security

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Detection performance of additive spread-spectrum watermarks depends on the statistical host signal model employed to derive the detection statistic. When transform coefficients are heavily quantized, the assumption of a Cauchy or Generalized Gaussian Distribution (GGD) is hard to justify and the estimation of model parameters becomes inaccurate. In this paper we derive a Likelihood-Ratio Test (LRT) based on the product of Bernoulli distributions. The watermark detector is designed to operate on quantized (integer) transform coefficients and therefore permits straightforward integration of the watermarking scheme in popular image and video codecs. Detection performance surpasses the linear correlation detector and is competitive with the computationally more demanding LRT based on a GGD.

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Section: Application Considerationsmentioning

confidence: 99%

Watermark detection on quantized transform coefficients using product bernoulli distributions

Meerwald

Uhl

2010

Proceedings of the 12th ACM Workshop on Multimedia and Security

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“…Without loss of generality, y and w * are assumed to be vectors of length N . In the literature [18,21,22,33], it is well established that the detection response ρ follows a Chi-square distribution with one degree of freedom (χ 2 1 ) under hypothesis H 0 , whereas under hypothesis H 1 , it follows a Non-Central Chi-square distribution with one degree of freedom and non-centrality parameter (χ 2 1, ), as shown in Fig. 4.…”

Section: Watermark Embedding and Detectionmentioning

confidence: 99%

“…After inspecting the Rao detector structure, there is only one parameter (i.e., the shape parameter β) to be estimated directly from the watermarked coefficients. However, as mentioned in [22,33], the detector presented by (11) is asymptotically optimal, which means that the host data needs to be adequately large. In this section, intensive experiments have been conducted to evaluate the performance of the proposed watermarking system on a set of test images.…”

Section: Watermark Embedding and Detectionmentioning

confidence: 99%

“…In this case, the role of the decoding scheme is to extract, bit by bit, the full version of the watermark in order to recover the hidden information [1, 7, 15, 16, 24-27, 35, 37, 39, 40, 43]. In one-bit watermarking, however, the watermark serves as a verification code where the role of the detector is to check the presence/absence of the watermark [2,13,21,22,30]. In practice, one-bit watermarking can be used in copy detection, copyright protection, and broadcast monitoring.…”

Section: Introductionmentioning

confidence: 99%

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Robust additive watermarking in the DTCWT domain based on perceptual masking

Zebbiche¹,

Khelifi

Loukhaoukha³

2018

Multimed Tools Appl

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In this paper, a robust additive image watermarking system operating in the Dual Tree Complex Wavelet Transform (DTCWT) domain is proposed. The system takes advantage of a new perceptual masking model that exploits the Human Visual System (HVS) characteristics at the embedding stage. It also uses an efficient watermark detection structure, called the Rao-test, to verify the presence of the candidate watermark. This structure relies on the statistical modeling of high frequency DTCWT coefficients by the Generalized Gaussian distribution. Experimental results show that the proposed system outperforms related state-of-the-art watermarking systems in terms of imperceptibility and robustness.

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“…The statistical analysis of the properties of correlator detector is used to derive an optimal fused correlator. Detection [7] based on additive watermarking in DWT domain is considered using spread spectrum technique. For watermark detection, two signal models and two hypothesis tests are considered.…”

Section: Introductionmentioning

confidence: 99%

Detection attack analysis using partial watermark in DCT domain

Gunjan

Laxmi

Gaur

2012

Proceedings of the Fifth International Conference on Security of Information and Networks

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The paper presents a novel framework for blind watermark detection on additive watermarking in Discrete Cosine Transform (DCT) domain. Watermarks which resist any attempt by an adversary to thwart their intended purpose such as unauthorized detection, removal or embedding of watermark are called secure watermarks. If an adversary is able to detect the watermark, he may further remove it or embed his own watermark. Thus, the ownership of the data cannot be claimed. The proposed scheme applies watermark detection as an attack and analyses the robustness of the scheme in respect of attack resistance. The scheme also puts forward the type of watermark that should be chosen to sustain such attacks on images. Moreover it also shows the conditions under which the watermark is resilient to unauthorized detection of watermark. The detection scheme uses a correlation based detector for a watermark assumed to be partially known to the attacker. The DCT of watermarked image is taken and the watermarked coefficients are extracted. The correlation coefficient between the extracted coefficients of watermarked image and the partial watermark known are calculated. Experimental results show that the proposed method is secure against unauthorized watermark detection.

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Lightweight Detection of Additive Watermarking in the DWT-Domain

Cited by 54 publications

References 27 publications

Watermark detection on quantized transform coefficients using product bernoulli distributions

Watermark detection on quantized transform coefficients using product bernoulli distributions

Robust additive watermarking in the DTCWT domain based on perceptual masking

Detection attack analysis using partial watermark in DCT domain

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