Hiding digital watermarks using multiresolution wavelet transform

Hsieh, Ming-Shing; Tseng, Din-Chang; Huang, Yong-Huai

doi:10.1109/41.954550

Cited by 260 publications

(9 citation statements)

References 26 publications

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“…Here we report all the standard methods found in the literature as illustrated in Table 1 and those used in this work to measure imperceptibility and robustness (along with capacity and computational cost) [34]. We use the methods for performance evaluation by ensuring [15] Discrete Wavelet Transform Robustness [16] Multiwavelet Transform Imperceptibility [17] Discrete Wavelet Transform Robustness [18] Fourier Transform Fidelity [19] Discrete wavelet Transform Imperceptibility [20] Discrete wavelet Transform Imperceptibility Frequency Domain + Radial Basis NN [21] Discrete Cosine Transform Robustness [22] Discrete wavelet Transform Imperceptible [23] Discrete wavelet Transform Invisible and Robust [24] Discrete wavelet Transform Robustness Frequency Domain + Hopfield NN [25] Capacity [26] Imperceptibility [27] Image Quality [28] Discrete Cosine Transform Invisible and Robust Frequency Domain + Full Counter Propagation NN [29] Robustness, Imperceptibility [30] Discrete Cosine Transform Robustness [31] Discrete Cosine Transform Complexity, Capacity, PSNR [32] Discrete Cosine Transform Imperceptibility, and robustness Frequency Domain + Synergetic NN [33] Discrete Wavelet Transform Robustness and Imperceptibility i) model and sources of distortion remain uniform ii)…”

Section: Literature Reviewmentioning

confidence: 99%

A Computational Modelling and Algorithmic Design Approach of Digital Watermarking in Deep Neural Networks

Kavitha¹,

Eranna²,

Giriprasad³

2020

Adv. sci. technol. eng. syst. j.

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In this paper we propose an algorithmic approach for Convolutional Neural Network (CNN) for digital watermarking which outperforms the existing frequency domain techniques in all aspects including security along with the criteria in the neural networks such as conditions embedded, and types of watermarking attack. This research addresses digital watermarking in deep neural networks and with comprehensive experiments through computational modeling and algorithm design, we examine the performance of the built system to demonstrate the potential of watermarking neural networks. The inability of intruder towards the retrieval of data without the knowledge of architecture and keys is also discussed and results of the proposed method are compared with the state of the art methods at different noises and attacks.

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Section: Literature Reviewmentioning

confidence: 99%

A Computational Modelling and Algorithmic Design Approach of Digital Watermarking in Deep Neural Networks

Kavitha¹,

Eranna²,

Giriprasad³

2020

Adv. sci. technol. eng. syst. j.

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“…The approximation component was then fed through another filter pair and down-sampled to the maximum decomposition level. This process is also known as multi-resolution analysis (MRA) [2,12,[28][29][30]. Therefore, the result of MRA is the detail component at each level and an approximation component at the highest level.…”

Section: Decomposition Of Prpd and Signal Energymentioning

confidence: 99%

Disturbance Elimination for Partial Discharge Detection in the Spacer of Gas-Insulated Switchgears

Wang

Kil

et al. 2017

Energies

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With the increasing demand for precise condition monitoring and diagnosis of gas-insulated switchgears (GISs), it has become a challenge to improve the detection sensitivity of partial discharge (PD) induced in the GIS spacer. This paper deals with the elimination of the capacitive component from the phase-resolved partial discharge (PRPD) signal generated in GIS spacers based on discrete wavelet transform (WT). Three types of typical insulation defects were simulated using PD cells. The single PD pulses were detected and were further used to determine the optimal mother wavelet. As a result, the bior6.8 was selected to decompose the PD signal into 8 levels and the signal energy at each level was calculated. The decomposed components related with capacitive disturbance were discarded, whereas those associated with PD were de-noised by a threshold and a thresholding function. Finally, the PRPD signals were reconstructed using the de-noised components.

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“…Hsieh and Tseng proposed a DWT-based algorithm in the following steps: an original image was decomposed into wavelet coefficients. Next, a multienergy watermarking scheme, based on the qualified significant wavelet tree, was used to achieve a robust algorithm [7]. Elbasi and Eskicioglu embedded a pseudorandom sequence as a watermark in 2 bands (LL and HH) using DWT [8].…”

Section: Related Workmentioning

confidence: 99%

A new approach of nonblind watermarking methods based on DWT and SVD via LU decomposition

Jane¹,

Elbaşi²

2014

Turk J Elec Eng & Comp Sci

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Multimedia security has been the focal point of considerable research activity in the last decade, mainly because of its wide application area. Watermarking, in particular, is identified as a major technology to achieve copyright protection and multimedia security. Therefore, recent studies in the literature include some evident approaches for embedding data into a multimedia element. Because of its useful frequency component separation, the discrete wavelet transform (DWT) is commonly used in watermarking schemes. Moreover, singular value decomposition (SVD) and lower-and-upper (LU) decomposition have little effect on the perception of the watermark. Therefore, in this study, a combination of DWT and SVD via LU decomposition is proposed as a new nonblind watermarking algorithm that requires cover work to detect the watermark. Experimental results show that the proposed algorithm is considerably robust and reliable against certain attacks without degrading the input image, by embedding a binary watermark on the low-low band. Moreover, the threshold values are data-dependent for watermarks after attacks; that is, the threshold value is always different from another for certain attacks so that similarity ratios in this algorithm, as a quality metric, are much more than those of the other algorithms consisting of DWT and/or SVD despite strong attacks causing lower peak signal-to-noise ratio values. Apart from robustness, reliability, and data-dependence, the other novel aspect of this study is to expand the application areas of watermarking with a new algorithm consisting of DWT, LU, and SVD, and this study will contribute to the literature for certain cases.

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Hiding digital watermarks using multiresolution wavelet transform

Cited by 260 publications

References 26 publications

A Computational Modelling and Algorithmic Design Approach of Digital Watermarking in Deep Neural Networks

A Computational Modelling and Algorithmic Design Approach of Digital Watermarking in Deep Neural Networks

Disturbance Elimination for Partial Discharge Detection in the Spacer of Gas-Insulated Switchgears

A new approach of nonblind watermarking methods based on DWT and SVD via LU decomposition

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