scholarly journals Compressive Sampling with Multiple Bit Spread Spectrum-Based Data Hiding

2020 ◽  
Vol 10 (12) ◽  
pp. 4338
Author(s):  
Gelar Budiman ◽  
Andriyan Bayu Suksmono ◽  
Donny Danudirdjo
Keyword(s):  
Data Hiding ◽  
Compression Ratio ◽  
Spread Spectrum ◽  
Sampling Technique ◽  
Singular Values ◽  
Compressive Sampling ◽  
Biomedical Signal ◽  
Audio Quality ◽  
Memory Efficiency ◽  
Broadcast Monitoring

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.

scholarly journals Compressive Sampling with Multiple Bits Spread Spectrum-Based Data Hiding

2020 ◽  
Author(s):  
Gelar Budiman ◽  
Andriyan Bayu Suksmono ◽  
Donny Danudirdjo
Keyword(s):  
Data Hiding ◽  
Compression Ratio ◽  
Spread Spectrum ◽  
Sampling Technique ◽  
Singular Values ◽  
Compressive Sampling ◽  
Biomedical Signal ◽  
Audio Quality ◽  
Memory Efficiency ◽  
Broadcast Monitoring

We propose a novel data hiding method in an audio host with a compressive sampling technique. An over-complete dictionary represents a group of the watermark. Each row of the dictionary is a Hadamard sequence representing multiple bits of the watermark. Then, the singular values of segment-based host audio in a diagonal matrix multiply 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 a 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 range 729-5292 bps with compression ratio 1.47-4.84 and perfect detected watermark.

Robust Image Data Hiding Technique for Copyright Protection

2013 ◽  
Vol 7 (2) ◽  
pp. 44-56 ◽  
Author(s):  
Siddharth Singh ◽  
Tanveer J. Siddiqui
Keyword(s):  
Data Hiding ◽  
Copyright Protection ◽  
Spread Spectrum ◽  
Image Data ◽  
Singular Values ◽  
Histogram Equalization ◽  
Chaotic Sequence ◽  
Discrete Wavelet ◽  
Robust Image ◽  
Value Decomposition

A robust image data-hiding scheme for copyright protection is proposed and simulated. The scheme uses a combination of redundant discrete wavelet transform (RDWT), singular value decomposition (SVD) and spread spectrum technique. The embedding is done by spreading the copyright mark into the singular values of middle frequency sub-bands of RDWT coefficients of the cover image. Chaotic sequence is used for spreading. The use of chaotic sequence and RDWT increases security and robustness of the proposed scheme. Simulation results show that the proposed scheme achieves higher security and robustness against filtering, addition of noise, JPEG compression, sharpening, gamma correction, resizing, rotation, and histogram equalization than other existing techniques for copyright protection.

scholarly journals Adaptive multimedia data hiding and watermarking

2021 ◽  
Author(s):  
Kan Li
Keyword(s):  
Data Hiding ◽  
Spread Spectrum ◽  
Optimization Algorithms ◽  
Wiener Filter ◽  
Lagrangian Function ◽  
Multimedia Data ◽  
Digital Information ◽  
Look Up Table ◽  
Uniform Quantization ◽  
Adaptive Multimedia

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.

Compressive Sampling for Detection of Frequency-Hopping Spread Spectrum Signals

2016 ◽  
Vol 64 (21) ◽  
pp. 5513-5524 ◽  
Author(s):  
Feng Liu ◽  
Michael W. Marcellin ◽  
Nathan A. Goodman ◽  
Ali Bilgin
Keyword(s):  
Spread Spectrum ◽  
Frequency Hopping ◽  
Compressive Sampling ◽  
Frequency Hopping Spread Spectrum ◽  
Spread Spectrum Signals

Security of spread-spectrum-based data hiding

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

Data Hiding for Stereo Audio Signals

2013 ◽  
pp. 104-128
Author(s):  
Kazuhiro Kondo
Keyword(s):  
Data Hiding ◽  
Audio Signal ◽  
Audio Coding ◽  
Original Signal ◽  
Audio Signals ◽  
Audio Quality ◽  
Input Source ◽  
Rate Conversion ◽  
Fixed Delay

This chapter proposes two data-hiding algorithms for stereo audio signals. The first algorithm embeds data into a stereo audio signal by adding data-dependent mutual delays to the host stereo audio signal. The second algorithm adds fixed delay echoes with polarities that are data dependent and amplitudes that are adjusted such that the interchannel correlation matches the original signal. The robustness and the quality of the data-embedded audio will be given and compared for both algorithms. Both algorithms were shown to be fairly robust against common distortions, such as added noise, audio coding, and sample rate conversion. The embedded audio quality was shown to be “fair” to “good” for the first algorithm and “good” to “excellent” for the second algorithm, depending on the input source.

scholarly journals Steganalysis of spread spectrum data hiding exploiting cover memory

2005 ◽  
Author(s):  
Kenneth Sullivan ◽  
Upamanyu Madhow ◽  
Shivkumar Chandrasekaran ◽  
Bangalore S. Manjunath
Keyword(s):  
Data Hiding ◽  
Spread Spectrum ◽  
Spectrum Data
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