A block-based RDWT-SVD image watermarking method using human visual system characteristics

Ernawan, Ferda; Kabir, Muhammad Nomani

doi:10.1007/s00371-018-1567-x

Cited by 82 publications

(58 citation statements)

References 36 publications

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“…Image feature refers to the mathematical means of characterizing digital image processing and is the foundation of image processing technology. Generally speaking, as researchers deepen their understanding of images, image representation methods have also undergone an evolutionary process from shallow to deep [31][32][33][34]. Among them, the shallow representation of the image is mainly embodied in intuitive forms such as color features, geometric features, and shape features.…”

Section: Image Features In Motion Detectionmentioning

confidence: 99%

[Retracted] Using Sensor Network in Motion Detection Based on Deep Full Convolutional Network Model

2021

Complexity

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Aiming at the shortcomings of traditional moving target detection methods in complex scenes such as low detection accuracy and high complexity, and not considering the overall structure information of the video frame image, this paper proposes a moving-target detection based on sensor network. First, a low-power motion detection wireless sensor network node is designed to obtain motion detection information in real time. Secondly, the background of the video scene is quickly extracted by the time domain averaging method, and the video sequence and the background image are channel-merged to construct a deep full convolutional network model. Finally, the network model is used to learn the deep features of the video scene and output the pixel-level classification results to achieve moving target detection. This method not only can adapt to complex video scenes of different sizes but also has a simple background extraction method, which effectively improves the detection speed.

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Section: Image Features In Motion Detectionmentioning

confidence: 99%

[Retracted] Using Sensor Network in Motion Detection Based on Deep Full Convolutional Network Model

2021

Complexity

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“…This paper proposes DWT-SVD for the multiple watermarking scheme. A DWT is a multiresolution for a mathematical tool that decompose the image [14][15][16][17][18]. Edges information is presented in the high frequency.…”

Section: Methodsmentioning

confidence: 99%

Multiple watermarking technique using optimal threshold

Alias

Ernawan

2020

IJEECS

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<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>

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“…In 2018, Ferda and Muhammad [24] proposed a blockbased RDWT-SVD method using human visual system (HVS) characteristics. This scheme presents an embedding method by examining the coefficients in the first column of U vectors.…”

Section: F Related Workmentioning

confidence: 99%

“…This process has been shortened during the extraction process as the position to extract secret information has already been set because of the saved coordinates of x and y. Table XIII shows that proposed work spent less computational time on embedding process than Ferda's [24] and Divya and Ranjan's [25] work. For the extraction process, proposed work spent a slightly longer time compared to Divya and Ranjan's and 24 seconds faster than Ferda's.…”

Section: Computational Timementioning

confidence: 99%

An Improved RDWT-based Image Steganography Scheme with QR Decomposition and Double Entropy

Ng¹,

Liew²,

Ernawan³

2020

IJACSA

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This paper introduces an improved RDWT-based image steganography with QR decomposition and double entropy system. It demonstrates image steganography method that hides grayscale secret image into grayscale cover image using RDWT, QR decomposition and entropy calculation. The proposed scheme made use of the human visual system (HVS) in the embedding process. Both cover and secret image are being segmented into non-overlapping blocks with identical block size. Then, entropy values generated from every image block will be sorted from the lowest value to the highest value. The embedding process starts by embedding the secret image block with lowest entropy value into the cover image block with lowest entropy value. The process goes on until all image blocks have been embedded. Embedding secret image into cover image according to the entropy values causes differences that HVS can less likely to detect because of the small changes on image texture. By applying the double entropy system, proposed scheme managed to achieve a higher PSNR value of 60.3773 while previous work gave a value of 55.5771. In terms of SSIM value, proposed scheme generated a value of 0.9998 comparing to previous work's value of 0.9967. The proposed scheme eliminated the false-positive issue and required low computational time of only 0.72 seconds for embedding and 1.14 seconds for extraction process. Also, it has shown better result compared to previous work in terms of imperceptibility.

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A block-based RDWT-SVD image watermarking method using human visual system characteristics

Cited by 82 publications

References 36 publications

[Retracted] Using Sensor Network in Motion Detection Based on Deep Full Convolutional Network Model

[Retracted] Using Sensor Network in Motion Detection Based on Deep Full Convolutional Network Model

Multiple watermarking technique using optimal threshold

An Improved RDWT-based Image Steganography Scheme with QR Decomposition and Double Entropy

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