Robust SVD-based schemes for medical image watermarking

Zermi, Narima; Khaldi, Amine; Kafi, Mohamed Redouane; Kahlessenane, Fares; Euschi, Salah

doi:10.1016/j.micpro.2021.104134

Cited by 48 publications

(18 citation statements)

References 29 publications

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“…In this paper, a 512 � 512 pixels medical image and a 32 � 32 pixels watermark image with information are used for simulation experiments, and the simulation platform is MATLAB 2022a. The normalised correlation coefficient (NC) and peak signal-to-noise ratio (PSNR) is used to observe the effect of extracting watermarks using this algorithm, Where NC indicates the degree of similarity between the two images before, this paper indicates the similarity between the original watermark and the extracted watermark, as shown in Equation (11), and the larger NC indicates the better robustness of the algorithm. PSNR represents the quality of the image, as shown in Equation (12), and this paper indicates the distortion level of the medical image after the attack.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…et al. [11] used DCT, Weber descriptor and Arnold chaotic map to propose a medical image watermarking algorithm based on Weber's law descriptors. In 2021, Zermi, Narima et al.…”

Section: Introductionmentioning

confidence: 99%

“…Trinadh Reddy, K.V.S.V. et al [11] used DCT, Weber descriptor and Arnold chaotic map to propose a medical image watermarking algorithm based on Weber's law descriptors. In 2021, Zermi, Narima et al [12] proposed a robust medical image watermarking algorithm based on SVD using DWT to decompose the image and apply SVD to LL, LH and HL, then to watermark the possible combinations of singular value matrices integration.…”

Section: Introductionmentioning

confidence: 99%

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Robust watermarking algorithm for medical images based on accelerated‐KAZE discrete cosine transform

Chen

et al. 2022

IET Biometrics

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With the continuous progress and development in the field of Internet technology, the area of medical image processing has also developed along with it. Specially, digital watermarking technology plays an essential role in the field of medical image processing and greatly improves the security of medical image information. A medical image watermarking algorithm based on an accelerated‐KAZE discrete cosine transform (AKAZE‐DCT) is proposed to address the poor robustness of medical image watermarking algorithms to geometric attacks, which leads to low security of the information contained in medical images. First, the AKAZE‐DCT algorithm is used to extract the feature vector of the medical image and then combined with the perceptual hashing technique to obtain the feature sequence of the medical image; then, the watermarking image is encrypted with logistic chaos dislocation to get the encrypted watermarking image, which ensures the security of the watermarking information; finally, the watermarking is embedded and extracted with the zero‐watermarking technique. The experimental results show that the algorithm can effectively extract the watermark under conventional and geometric attacks, reflecting better robustness and invisibility, and has certain practicality in the medical field compared with other algorithms.

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Section: Experiments and Resultsmentioning

confidence: 99%

“…et al. [11] used DCT, Weber descriptor and Arnold chaotic map to propose a medical image watermarking algorithm based on Weber's law descriptors. In 2021, Zermi, Narima et al.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robust watermarking algorithm for medical images based on accelerated‐KAZE discrete cosine transform

Chen

et al. 2022

IET Biometrics

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show abstract

“…SVD is a numerical analysis method that is widely used for various image processing applications [18], which can also be implemented in video watermarking [1,19,20]. This method performs a numerical analysis by decomposing the original matrix (𝑍) with size (𝑀 × 𝑁) of rank 𝑟 (the rank of a complex matrix Z that represents a specific value of a non-zero singular value) into three matrices of the same size.…”

Section: Singular Value Decomposition (Svd)mentioning

confidence: 99%

Imperceptible Improvement using Edge Area Selection for Robust Video Watermarking Using Tchebichef - Singular Value Decomposition

2022

IJIES

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Watermarking is a technology to protect the copyright of multimedia data. Video is one of the most complex media compared to image or audio. In invisible watermarking, imperceptible aspects are important besides robustness. This research proposes a frame selection method based on the edge area. The frame with the most edge area is selected and used for watermark embedding with Tchebichef transformation and singular value decomposition. A dual-stage Arnold transform was carried out to spread the edge area, and the watermark was embedded more evenly to improve the imperceptibility quality. Based on the test results, this method can improve the peak signal to noise ratio (PSNR) quality by around 1dB to 2dB while still maintaining the robustness of the watermark compared to the previous method without edge area selection, and dual-stage Arnold transform.

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“…The singular value decomposition (SVD) can update the atoms and coefficients of the dictionary, and it is used to find the matrix with the closest distances, reducing the reconstruction error of the signal. Then, the singular value decomposition is used to apply to E k [43]. To ensure the accuracy of signal reconstruction and sparse constraints, only x k the non-zero items are retained, and all other zero items are removed.…”

Section: Pattern Recognition Based On K-svdmentioning

confidence: 99%

An Improved Dictionary-Based Method for Gas Identification with Electronic Nose

et al. 2022

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The dictionary learning algorithm has been successfully applied to electronic noses because of its high recognition rate. However, most dictionary learning algorithms use l0-norm or l1-norm to regularize the sparse coefficients, which means that the electronic nose takes a long time to test samples and results in the inefficiency of the system. Aiming at accelerating the recognition speed of the electronic nose system, an efficient dictionary learning algorithm is proposed in this paper where the algorithm performs a multi-column atomic update. Meanwhile, to solve the problem that the singular value decomposition of the k-means (K-SVD) dictionary has little discriminative power, a novel classification model is proposed, a coefficient matrix is achieved by a linear projection to the training sample, and a constraint is imposed where the coefficients in the same category should keep a large coefficient and be closer to their class centers while coefficients in the different categories should keep sparsity. The algorithm was evaluated and analyzed based on the comparisons of several traditional classification algorithms. When the dimension of the sample was larger than 10, the average recognition rate of the algorithm was maintained above 92%, and the average training time was controlled within 4 s. The experimental results show that the improved algorithm is an effective method for the development of an electronic nose.

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Robust SVD-based schemes for medical image watermarking

Cited by 48 publications

References 29 publications

Robust watermarking algorithm for medical images based on accelerated‐KAZE discrete cosine transform

Robust watermarking algorithm for medical images based on accelerated‐KAZE discrete cosine transform

Imperceptible Improvement using Edge Area Selection for Robust Video Watermarking Using Tchebichef - Singular Value Decomposition

An Improved Dictionary-Based Method for Gas Identification with Electronic Nose

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