Research and Simulation of Performance of W-SVD Watermarking Algorithm Based on Color Images

2014 ◽  
Vol 513-517 ◽  
pp. 1980-1983
Author(s):  
Hou Xiao Fang ◽  
Zhang Chun E
Keyword(s):  
Singular Value Decomposition ◽  
Digital Watermarking ◽  
Wavelet Decomposition ◽  
Low Frequency ◽  
Singular Value ◽  
Perceptual Quality ◽  
Discrete Wavelet ◽  
Discrete Wavelet Decomposition ◽  
Value Decomposition ◽  
Research And Design

The paper presents an algorithm for digital watermarking in colored image, which combined with singular value decomposition and discrete wavelet transform, it selects R-vector of colored image to do discrete wavelet decomposition, and extracts the low-frequency coefficients to finish singular value decomposition and generate watermark template, then reconstructs image after embed watermarking, completes the process of embedding watermarking. The algorithm makes full use of the excellent properties of the wavelet and singular value decomposition, which makes good performance for watermarking system. To this algorithm, the paper studied from two aspects of the image perceptual quality and robustness, and analyze the influence of the parameters in the performance of watermarking algorithm, a number of experiments and data show that these values may bring essential influence to the performance of digital watermarking system, also explains the importance of value selection in the algorithm. The conclusion of the paper is typical and universal, has certain reference significance to research and design the watermarking algorithm.

A Framework for Image Encryption on Frequency Domain

2019 ◽  
pp. 247-259
Author(s):  
Zhe Liu ◽  
Mee Loong Yang ◽  
Wei Qi Yan
Keyword(s):  
Image Encryption ◽  
Digital Watermarking ◽  
Logistic Map ◽  
Low Frequency ◽  
Singular Value ◽  
Encryption Algorithm ◽  
Discrete Wavelet ◽  
Secret Key ◽  
Value Decomposition ◽  
Image Encryption Algorithm

In this chapter, the authors propose an improved image encryption algorithm based on digital watermarking. The algorithm combines discrete wavelet transform (DWT), discrete cosine transform (DCT), and singular value decomposition (SVD) together in a DWT-DCT-SVD framework to improve the robust watermarking technique. The secret image is embedded into both high-frequency and low-frequency sub-bands of the host image; this makes it difficult to be attacked in all the sub-bands. To reduce the size of a secret key, the authors use a logistic map to generate random images so as to replace the host images. They tested the algorithm by using five types of attacks and the results indicate that the proposed algorithm has higher robustness than traditional chaotic scrambling method and the DRPE method. It shows strong resilience against the five types of attacks as well as statistical attacks.

Research of Digital Watermarking Algorithm Based on SVD and DWT

2011 ◽  
Vol 65 ◽  
pp. 385-388
Author(s):  
Ping Yang ◽  
Liang Yu Yang ◽  
Yu Jie Zhang
Keyword(s):  
Digital Watermarking ◽  
Low Frequency ◽  
Singular Values ◽  
Singular Value ◽  
Secret Message ◽  
Discrete Wavelet ◽  
Information Redundancy ◽  
Digital Multimedia ◽  
Multi Media ◽  
Value Decomposition

In order to protect the integrity and copyright, digital watermarking embeded secret message into the digital multi- media, which is not easy to detect the embedded watermarking, but determine the distinction using of information redundancy and randomness of digital multimedia. According to the characteristics of digital watermarking, the singular value decomposition (SVD) and discrete wavelet transform (DWT) algorithm are combined using the characteristics of SVD of image matrix and the features of multi-resolution of DWT. Therefore, a new digital watermarking algorithm of SVD is proposed in this paper. The singular values of each band of watermarking image are embedded into the coefficients of corresponding to the singular value of original image low-frequency band. The experimental results showed that the algorithm of the watermarking embedding and extraction had good robustness and effectiveness. The watermarking was robust and transparent.

A Framework for Image Encryption on Frequency Domain

2021 ◽  
pp. 328-338
Author(s):  
Zhe Liu ◽  
Mee Loong Yang ◽  
Wei Qi Yan
Keyword(s):  
Image Encryption ◽  
Digital Watermarking ◽  
Logistic Map ◽  
Low Frequency ◽  
Singular Value ◽  
Encryption Algorithm ◽  
Discrete Wavelet ◽  
Secret Key ◽  
Value Decomposition ◽  
Image Encryption Algorithm

In this chapter, the authors propose an improved image encryption algorithm based on digital watermarking. The algorithm combines discrete wavelet transform (DWT), discrete cosine transform (DCT), and singular value decomposition (SVD) together in a DWT-DCT-SVD framework to improve the robust watermarking technique. The secret image is embedded into both high-frequency and low-frequency sub-bands of the host image; this makes it difficult to be attacked in all the sub-bands. To reduce the size of a secret key, the authors use a logistic map to generate random images so as to replace the host images. They tested the algorithm by using five types of attacks and the results indicate that the proposed algorithm has higher robustness than traditional chaotic scrambling method and the DRPE method. It shows strong resilience against the five types of attacks as well as statistical attacks.

scholarly journals Integrated Discrete Wavelet Transform and Singular Value Decomposition based Biometric Watermarking for Authentication using Iris Images

2019 ◽  
Vol 8 (4) ◽  
pp. 4672-4679
Keyword(s):  
Wavelet Transform ◽  
Singular Value Decomposition ◽  
Digital Watermarking ◽  
Singular Value ◽  
The Other ◽  
Discrete Wavelet ◽  
Transform Domain ◽  
Biometric Recognition ◽  
Value Decomposition ◽  
Biometric Information

Recently with respect to developments in watermarking techniques, intruders are capable of accessing the database. Several strategies are designed for securing information. Utilization of watermarks will be recommended with the background pertaining to several biometric strategies comprising fingerprints, position of palm, gait, iris, speech, etc. Among the available approaches digital watermarking will be one of the effective techniques. Prominent manner of carrying out the digital watermarking will be spatial domain along with strong transform domain. This paper establishes a strategy for providing the watermark, besides biometric information by utilizing Wavelet Transform in addition to Singular Value Decomposition. Utilization of biometric rather than conservative watermark enhances protection of information. The utilized biometric will be iris. Consideration of iris template as watermark guarantees iris pattern communication, whereas watermarking iris images might be involved in assisting, safeguarding of information in addition to identification of iris image moderation. The mentioned work involved in carrying out analysis means checking the authentication under various attacks along with no attacks. Motivation in utilization of watermarking will depend on enhancement in the field of biometric recognition. On the other hand, utilization of biometric patterns as “message” has to be designed with conservative strong watermarking for safeguarding the information. So as to ensure biometric appreciation to the development of watermark.

scholarly journals Biosignal Compression Toolbox for Digital Biomarker Discovery

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 516
Author(s):  
Brinnae Bent ◽  
Baiying Lu ◽  
Juseong Kim ◽  
Jessilyn P. Dunn
Keyword(s):  
Wavelet Transform ◽  
Singular Value Decomposition ◽  
Data Compression ◽  
Biomarker Discovery ◽  
Singular Value ◽  
Sensor Data ◽  
Discrete Wavelet ◽  
Wearable Sensor ◽  
Huffman Encoding ◽  
Value Decomposition

A critical challenge to using longitudinal wearable sensor biosignal data for healthcare applications and digital biomarker development is the exacerbation of the healthcare “data deluge,” leading to new data storage and organization challenges and costs. Data aggregation, sampling rate minimization, and effective data compression are all methods for consolidating wearable sensor data to reduce data volumes. There has been limited research on appropriate, effective, and efficient data compression methods for biosignal data. Here, we examine the application of different data compression pipelines built using combinations of algorithmic- and encoding-based methods to biosignal data from wearable sensors and explore how these implementations affect data recoverability and storage footprint. Algorithmic methods tested include singular value decomposition, the discrete cosine transform, and the biorthogonal discrete wavelet transform. Encoding methods tested include run-length encoding and Huffman encoding. We apply these methods to common wearable sensor data, including electrocardiogram (ECG), photoplethysmography (PPG), accelerometry, electrodermal activity (EDA), and skin temperature measurements. Of the methods examined in this study and in line with the characteristics of the different data types, we recommend direct data compression with Huffman encoding for ECG, and PPG, singular value decomposition with Huffman encoding for EDA and accelerometry, and the biorthogonal discrete wavelet transform with Huffman encoding for skin temperature to maximize data recoverability after compression. We also report the best methods for maximizing the compression ratio. Finally, we develop and document open-source code and data for each compression method tested here, which can be accessed through the Digital Biomarker Discovery Pipeline as the “Biosignal Data Compression Toolbox,” an open-source, accessible software platform for compressing biosignal data.

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