scholarly journals Multiple watermarking technique using optimal threshold

2020 ◽  
Vol 18 (1) ◽  
pp. 368
Author(s):  
Noruhida Alias ◽  
Ferda Ernawan
Keyword(s):  
Cross Correlation ◽  
Orthogonal Matrix ◽  
Arnold Transform ◽  
High Demand ◽  
Trade Off ◽  
Multiple Watermarking ◽  
Jpeg2000 Compression ◽  
Optimal Thresholds ◽  
Multiple Watermarks ◽  
Watermarking Scheme

<span>The multiple watermarking technique has drawn more attention due to high demand for embedding more than one copyright. This paper proposes a multiple watermarking scheme using DWT-SVD by utilizing HVS characteristics. Our scheme embeds multiple watermarks in red and blue colors. The proposed scheme examines the coefficients of U orthogonal matrix for embedding and extracting watermarks. Watermarks are scrambled by Arnold transform before embedded in the host image in order provide additional security. The optimal thresholds for red and blue colors are revealed by finding a trade-off between normalized-cross correlation and imperceptibility from quantization steps. The experimental results demonstrate that our scheme achieves high resistant under JPEG and JPEG2000 compression for both inserted watermarks. </span>

scholarly journals A Blind Multiple Watermarks based on Human Visual Characteristics

2018 ◽  
Vol 8 (4) ◽  
pp. 2578 ◽  
Author(s):  
Ferda Ernawan ◽  
Siau-Chuin Liew ◽  
Zuriani Mustaffa ◽  
Kohbalan Moorthy
Keyword(s):  
Cross Correlation ◽  
Threshold Value ◽  
Different Types ◽  
Multiple Watermarks ◽  
Visual Characteristics ◽  
Block Based ◽  
Value Decomposition ◽  
Watermarking Scheme ◽  
Normalize Cross Correlation ◽  
Human Visual Characteristics

<span lang="EN-MY">Digital watermarking is an alternative solution to prevent unauthorized duplication, distribution and breach of ownership right. </span><span>This paper proposes a watermarking scheme for multiple watermarks embedding. The embedding of multiple watermarks use a block-based scheme based on human visual characteristics. A threshold is used to determine the watermark values by modifying first column of the orthogonal <em>U</em> matrix obtained from Singular Value Decomposition (SVD). The tradeoff between normalize cross-correlation and imperceptibility of watermarked image from quantization steps was used to achieve an optimal threshold value. The results show that our proposed multiple watermarks scheme exhibit robustness against signal processing attacks. The proposed scheme demonstrates that the watermark recovery from chrominance blue was resistant against different types of attacks.</span>

scholarly journals Multiple Watermarking Scheme for Video & Image for Authentication & Copyright Protection

2021 ◽  
Vol 1131 (1) ◽  
pp. 012020
Author(s):  
Neha Janu ◽  
Ankit Kumar ◽  
Pankaj Dadheech ◽  
Gajanand Sharma ◽  
Ashutosh Kumar ◽  
...  
Keyword(s):  
Copyright Protection ◽  
Video Image ◽  
Multiple Watermarking ◽  
Watermarking Scheme

scholarly journals An Efficient Robust Multiple Watermarking Algorithm for Vector Geographic Data

Information ◽  
2018 ◽  
Vol 9 (12) ◽  
pp. 296 ◽  
Author(s):  
Yingying Wang ◽  
Chengsong Yang ◽  
Changqing Zhu ◽  
Kaimeng Ding
Keyword(s):  
Detection Efficiency ◽  
Detection Threshold ◽  
Detection Algorithm ◽  
Multiple Watermarking ◽  
Detection Model ◽  
Detection Algorithms ◽  
Geographic Data ◽  
Multiple Watermarks ◽  
Watermark Algorithm ◽  
Made In

Vector geographic data play an important role in location information services. Digital watermarking has been widely used in protecting vector geographic data from being easily duplicated by digital forensics. Because the production and application of vector geographic data refer to many units and departments, the demand for multiple watermarking technology is increasing. However, multiple watermarking algorithm for vector geographic data draw less attention, and there are many urgent problems to be solved. Therefore, an efficient robust multiple watermark algorithm for vector geographic data is proposed in this paper. The coordinates in vector geographic data are first randomly divided into non-repetitive sets. The multiple watermarks are then embedded into the different sets. In watermark detection correlation, the Lindeberg theory is used to build a detection model and to confirm the detection threshold. Finally, experiments are made in order to demonstrate the detection algorithm, and to test its robustness against common attacks, especially against cropping attacks. The experimental results show that the proposed algorithm is robust against the deletion of vertices, addition of vertices, compression, and cropping attacks. Moreover, the proposed detection algorithm is compatible with single watermarking detection algorithms, and it has good performance in terms of detection efficiency.

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