Robust Zero-Watermarking Algorithm for Medical Images Using Double-Tree Complex Wavelet Transform and Hessenberg Decomposition

Huang, Tongyuan; Xu, Jia; Yang, Yibo; Han, Baoru

doi:10.3390/math10071154

Cited by 21 publications

(17 citation statements)

References 23 publications

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“…Classic methods can include methods, such as those based on the least significant bit [ 23 , 24 , 25 ] or methods based on frequency domain manipulation (discrete wavelet transform, discrete Fourier transform, discrete sine, and cosine transform) [ 10 , 26 , 27 , 28 , 29 , 30 , 31 ]. There are many modifications of these methods, such as a combination of the least significant bit method and those based on manipulation of the frequency domain, which includes publications concerning hybrid domains [ 23 , 32 , 33 , 34 ].…”

Section: Literature Reviewmentioning

confidence: 99%

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High-Quality Video Watermarking Based on Deep Neural Networks for Video with HEVC Compression

Kaczyński

Piotrowski

Pietrow

2022

Sensors

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This article presents a method for transparent watermarking of high-capacity watermarked video under H.265/HEVC (High-Efficiency Video Coding) compression conditions while maintaining high-quality encoded image. The aim of this paper is to present a method for watermark embedding using neural networks under conditions of subjecting video to lossy compression of the HEVC codec using the YUV420p color model chrominance channel for watermarking. This paper presents a method for training a deep neural network to embed a watermark when a compression channel is present. The discussed method is characterized by high accuracy of the video with an embedded watermark compared to the original. The PSNR (peak signal-to-noise ratio) values obtained are over 44 dB. The watermark capacity is 96 bits for an image with a resolution of 128 × 128. The method enables the complete recovery of a watermark from a single video frame compressed by the HEVC codec within the range of compression values defined by the CRF (constant rate factor) up to 22.

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Section: Literature Reviewmentioning

confidence: 99%

“…When discussing the embedding of a watermark in an image, a distinction should be made between embedding a watermark in a static image [ 23 , 34 , 35 , 36 , 37 ] and in a video [ 14 , 15 , 16 , 17 ]. The methods used for static images find their application and development in methods for video purposes.…”

Section: Literature Reviewmentioning

confidence: 99%

High-Quality Video Watermarking Based on Deep Neural Networks for Video with HEVC Compression

Kaczyński

Piotrowski

Pietrow

2022

Sensors

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“…To verify the advantage of our proposed algorithm, we used the same experiment condition to compare it with other representative works [23,28,30,31]. In the contrast experiment, the 128 × 128 brain image shown Fig.…”

Section: Comparisons With Other Algorithmsmentioning

confidence: 99%

“…Dai et al [22] proposed a hybrid reversible zero-watermarking (HRZW), in which the authors combined the zerowatermarking and reversible watermarking to generate the ownership shares through the characteristics and watermarking information of the nearest neighbor gray residue (NNGR), and then reversibly embed the generated ownership shares based on slantlet transform, singular value decomposition and quantization index modulation (SLT-SVD-QIM). For medical images, a robust zero-watermarking algorithm by fusing Dual-Tree Complex Wavelet Transform (DTCWT), Hessenberg decomposition, and Multi-level Discrete Cosine Transform (MDCT) was proposed by Huang et al [23].…”

Section: Introductionmentioning

confidence: 99%

Application of Zero-Watermarking for Medical Image in Intelligent Sensor Network Security

Tu¹,

Jia²,

Du³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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The field of healthcare is considered to be the most promising application of intelligent sensor networks. However, the security and privacy protection of medical images collected by intelligent sensor networks is a hot problem that has attracted more and more attention. Fortunately, digital watermarking provides an effective method to solve this problem. In order to improve the robustness of the medical image watermarking scheme, in this paper, we propose a novel zero-watermarking algorithm with the integer wavelet transform (IWT), Schur decomposition and image block energy. Specifically, we first use IWT to extract low-frequency information and divide them into non-overlapping blocks, then we decompose the sub-blocks by Schur decomposition. After that, the feature matrix is constructed according to the relationship between the image block energy and the whole image energy. At the same time, we encrypt watermarking with the logistic chaotic position scrambling. Finally, the zero-watermarking is obtained by XOR operation with the encrypted watermarking. Three indexes of peak signal-to-noise ratio, normalization coefficient (NC) and the bit error rate (BER) are used to evaluate the robustness of the algorithm. According to the experimental results, most of the NC values are around 0.9 under various attacks, while the BER values are very close to 0. These experimental results show that the proposed algorithm is more robust than the existing zero-watermarking methods, which indicates it is more suitable for medical image privacy and security protection.

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“…In the literature, there are many publications on embedding a watermark in a static image using the classical methods [ 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 ]. These methods use the whole image or some of its regions, constituting the areas of interest.…”

Section: Literature Reviewmentioning

confidence: 99%

High-Quality Video Watermarking Based on Deep Neural Networks and Adjustable Subsquares Properties Algorithm

Kaczyński

Piotrowski

2022

Sensors

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This paper presents a method of high-capacity and transparent watermarking based on the usage of deep neural networks with the adjustable subsquares properties algorithm to encode the data of a watermark in high-quality video using the H.265/HEVC (High-Efficiency Video Coding) codec. The aim of the article is to present a method of embedding a watermark in a video with HEVC codec compression by making changes in a video in a way that is not noticeable to the naked eye. The method presented here is characterised by focusing on ensuring the accuracy of the original image in relation to the watermarked image, providing the transparency of the embedded watermark, while ensuring its survival after compression by the HEVC codec. The article includes a presentation of the practical results of watermark embedding with a built-in variation mechanism of its capacity and resistance, thanks to the adjustable subsquares properties algorithm. The obtained PSNR (peak signal-to-noise ratio) results are at the level of 40 dB or better. There is the possibility of the complete recovery of a watermark from a single frame compressed in the CRF (constant rate factor) range of up to 16, resulting in a BER (bit error rate) equal to 0 for the received watermark.

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Robust Zero-Watermarking Algorithm for Medical Images Using Double-Tree Complex Wavelet Transform and Hessenberg Decomposition

Cited by 21 publications

References 23 publications

High-Quality Video Watermarking Based on Deep Neural Networks for Video with HEVC Compression

High-Quality Video Watermarking Based on Deep Neural Networks for Video with HEVC Compression

Application of Zero-Watermarking for Medical Image in Intelligent Sensor Network Security

High-Quality Video Watermarking Based on Deep Neural Networks and Adjustable Subsquares Properties Algorithm

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