A lightweight rao-cauchy detector for additive watermarking in the dwt-domain

Kwitt, Roland; Meerwald, Peter; Uhl, Andreas

doi:10.1145/1411328.1411337

Cited by 19 publications

(9 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A second model which has recently appeared in literature is the two-parameter Cauchy distribution, which is a member of the family of SαS distributions [7] (with α = 1). This model has already been successfully employed for blind DCTdomain watermarking utilizing low-to mid-frequency DCT coefficients [4] and DWT detail subband coefficients [9]. The PDF of the Cauchy distribution with location parameter −∞ < δ < ∞ and shape parameter γ > 0 is given by [13]:…”

Section: A Statistical Models For Host Signal Noisementioning

confidence: 99%

“…Under the alternative hypothesis both Rao test statistics follow a non-central Chi-Square distribution χ 2 1,λ with one degree of freedom and non-centrality parameter λ, see [2] and [9], respectively. In Section IV we will see how to estimate λ from the experimental detection responses to analyze the performance.…”

Section: Threshold Determinationmentioning

confidence: 99%

“…for large data records) and does not depend on knowledge about the embedding power any more. In [9] the same scheme was employed to derive a watermark detector in Cauchy distributed noise. Since detection runtime requirements are important to certain applications we are not only concerned about the detection performance of the watermark detectors but also about their computational behavior.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Lightweight Detection of Additive Watermarking in the DWT-Domain

Kwitt

Meerwald

Uhl

2011

IEEE Trans. on Image Process.

Self Cite

View full text Add to dashboard Cite

Abstract-This article aims at lightweight, blind detection of additive spread-spectrum watermarks in the DWT domain. We focus on two host signal noise models and two types of hypothesis tests for watermark detection. As a crucial point of our work we take a closer look at the computational requirements of watermark detectors. This involves the computation of the detection response, parameter estimation and threshold selection. We show that by switching to approximate host signal parameter estimates or even fixed parameter settings we achieve a remarkable improvement in runtime performance without sacrificing detection performance. Our experimental results on a large number of images confirm the assumption that there is not necessarily a trade-off between computation time and detection performance.

show abstract

Section: A Statistical Models For Host Signal Noisementioning

confidence: 99%

Section: Threshold Determinationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Lightweight Detection of Additive Watermarking in the DWT-Domain

Kwitt

Meerwald

Uhl

2011

IEEE Trans. on Image Process.

Self Cite

View full text Add to dashboard Cite

show abstract

“…For efficient blind watermark detection, accurate modeling of the host signal is required. We assume a Cauchy distribution of the DCT coefficients [1] and chose the Rao-Cauchy (RC) detector [6] whose detection statistic for the received signal Y l of length L and the test against a detection threshold T are given by…”

Section: Watermarking Of H264-encoded Videomentioning

confidence: 99%

Robust Watermarking of H.264/SVC-Encoded Video: Quality and Resolution Scalability

Meerwald

Uhl

2011

Digital Watermarking

Self Cite

View full text Add to dashboard Cite

Abstract. In this paper we investigate robust watermarking integrated with H.264/SVC video coding and address coarse-grain quality and spatial resolution scalability features according to Annex G of the H.264 standard. We show that watermark embedding in the base layer of the video is insufficient to protect the decoded video content when enhancements layers are employed. The problem is mitigated by a propagation technique of the base layer watermark signal when encoding the enhancement layer. In case of spatial resolution scalability, the base layer watermark signal is upsampled to match the resolution of the enhancement layer data. We demonstrate blind watermark detection in the fulland low-resolution decoded video for the same adapted H.264/SVC bitstream and, surprisingly, can report bit rate savings when extending the base layer watermark to the enhancement layer.

show abstract

“…It is applied first in the detection of additive [17] and then multiplicative [18] watermarks. In [19], the wavelet coefficients are modelled as Cauchy distribution and a Rao detector is proposed. In [20], a locally adaptive image model has been proposed.…”

Section: Introductionmentioning

confidence: 99%