scholarly journals Adaptive multimedia data hiding and watermarking

Author(s):  
Kan Li

Watermarking is a technique of hiding a message about a work of media within that work itself in· the purpose of protecting the digital information against illegal duplication and manipulation. The objectives of this study are to analyze the robustness and distortion performance of watermarking system and to explore watermarking schemes which balance the robustness-distortion tradeoff optimally. In this thesis, We present a detector algorithm to adaptively extract spread spectrum watermark by filtering the watermarked images with Wiener filter. Two optimization algorithms for quantization watermarking are proposed. First one optimizes uniform quantization based look-up table embedding which minimizes watermarking distortion. Secondly, we analyze the robustness-distortion tradeoff and formulate the robustness-distortion tradeoff into a Lagrangian function. Hence optimal quantizers for watermarking subject to given robustness or fidelity constraint are achieved.

2021 ◽  
Author(s):  
Kan Li

Watermarking is a technique of hiding a message about a work of media within that work itself in· the purpose of protecting the digital information against illegal duplication and manipulation. The objectives of this study are to analyze the robustness and distortion performance of watermarking system and to explore watermarking schemes which balance the robustness-distortion tradeoff optimally. In this thesis, We present a detector algorithm to adaptively extract spread spectrum watermark by filtering the watermarked images with Wiener filter. Two optimization algorithms for quantization watermarking are proposed. First one optimizes uniform quantization based look-up table embedding which minimizes watermarking distortion. Secondly, we analyze the robustness-distortion tradeoff and formulate the robustness-distortion tradeoff into a Lagrangian function. Hence optimal quantizers for watermarking subject to given robustness or fidelity constraint are achieved.


Informatica ◽  
2007 ◽  
Vol 18 (4) ◽  
pp. 615-628 ◽  
Author(s):  
Jeng-Shyang Pan ◽  
Hao Luo ◽  
Zhe-Ming Lu

2007 ◽  
Author(s):  
Luis Pérez-Freire ◽  
Pierre Moulin ◽  
Fernando Pérez-González
Keyword(s):  

Data Hiding ◽  
2013 ◽  
pp. 69-90
Author(s):  
Michael Raggo ◽  
Chet Hosmer
Keyword(s):  

2020 ◽  
Vol 10 (12) ◽  
pp. 4338
Author(s):  
Gelar Budiman ◽  
Andriyan Bayu Suksmono ◽  
Donny Danudirdjo

We propose a novel data hiding method in an audio host with a compressive sampling technique. An over-complete dictionary represents a group of watermarks. Each row of the dictionary is a Hadamard sequence representing multiple bits of the watermark. Then, the singular values of the segment-based host audio in a diagonal matrix are multiplied by the over-complete dictionary, producing a lower size matrix. At the same time, we embed the watermark into the compressed audio. In the detector, we detect the watermark and reconstruct the audio. This proposed method offers not only hiding the information, but also compressing the audio host. The application of the proposed method is broadcast monitoring and biomedical signal recording. We can mark and secure the signal content by hiding the watermark inside the signal while we compress the signal for memory efficiency. We evaluate the performance in terms of payload, compression ratio, audio quality, and watermark quality. The proposed method can hide the data imperceptibly, in the range of 729–5292 bps, with a compression ratio 1.47–4.84, and a perfectly detected watermark.


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