scholarly journals FAST IMPLEMENTATION OF DIGITAL WATERMARKING SCHEMES BASED ON ARNOLD AND DISCRETE WAVELET TRANSFORMS

Author(s):  
A. G. Zotin ◽  
A. V. Proskurin

Abstract. In recent years, digital watermarking of photo and video materials has become more and more important in connection with the transmission of multimedia data over unsecured communication channels. Digital watermarking allows to reduce the amount of transmitted information and to protect embedded metadata. Improving robustness and security of embedded data increases computational costs, which obstruct usage of digital watermarks in mobile devices. In this research, we propose a number of improvements to the digital watermarking process based on Arnold and discrete wavelet transforms to reduce the computational cost. Considering the watermark as a linear sequence of pixels allows us to speed up its processing. The two-dimensional lookup table allows performing an Arnold transform in constant time regardless of the number of iterations. Number of iteration for each block of watermark is determined using hash function applied to the secret key. Also, the structure of the lookup table is proposed to accelerate the embedding of watermark. This table allows to determine the frequency coefficients for embedding based on the key hash code. Proposed improvements allow to speed up the watermark preparation by an average 14 times and the overall embedding process by 1.22 times for 1920×1080 images.

2008 ◽  
Vol 08 (03) ◽  
pp. 351-368 ◽  
Author(s):  
RASHMI AGARWAL ◽  
M. S. SANTHANAM

Many current watermarking algorithms insert data in the spatial or transform domains like the discrete cosine, the discrete Fourier, and the discrete wavelet transforms. In this paper, we present a data-hiding algorithm that exploits the singular value decomposition (SVD) representation of the data. We compute the SVD of the host image and the watermark and embed the watermark in the singular vectors of the host image. The proposed method leads to an imperceptible scheme for digital images, both in grey scale and color and is quite robust against attacks like noise and JPEG compression.


Author(s):  
Maya M. Lyasheva ◽  
Stella A. Lyasheva ◽  
Mikhail P. Shleymovich

2020 ◽  
Author(s):  
Victor Biazon ◽  
Reinaldo Bianchi

Trading in the stock market always comes with the challenge of deciding the best action to take on each time step. The problem is intensified by the theory that it is not possible to predict stock market time series as all information related to the stock price is already contained in it. In this work we propose a novel model called Discrete Wavelet Transform Gated Recurrent Unit Network (DWT-GRU). The model learns from the data to choose between buying, holding and selling, and when to execute them. The proposed model was compared to other recurrent neural networks, with and without wavelets preprocessing, and the buy and hold strategy. The results shown that the DWT-GRU outperformed all the set baselines in the analysed stocks of the Brazilian stock market.


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