Lossless medical image watermarking method based on significant difference of cellular automata transform coefficient

Fan, Tzuo-Yau; Chao, Her-Chang; Chieu, Bin-Chang

doi:10.1016/j.image.2018.09.015

Cited by 46 publications

(14 citation statements)

References 21 publications

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“…As for the DCT transform which are used in coding, Wavelet transforms have undoubtedly gained significant popularity in the image watermarking community [ 14 ]. The psychovisually analyses satisfactorily performed optimize the encoders’ quantization tables and the multi-scale aspect of such transforms is suitable for a more robust distribution of the watermark [ 19 ].…”

Section: Related Workmentioning

confidence: 99%

A lossless DWT-SVD domain watermarking for medical information security

Zermi

Khaldi

Kafi

et al. 2021

Multimed Tools Appl

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The goal of this work is to protect as much as possible the images exchanged in telemedicine, to avoid any confusion between the patient’s radiographs, these images are watermarked with the patient’s information as well as the acquisition data. Thus, during the extraction, the doctor will be able to affirm with certainty that the images belong to the treated patient. The ultimate goal of our completed work is to properly integrate the watermark with as little distortion as possible to typically retain the medical information in the image. In this innovative approach used DWT decomposition is appropriately applied to the image which allows a remarkably satisfactory adjustment during the insertion. An SVD is then applied to the three subbands LL, LH and HL, which ideally allows retaining the maximum energy of the used image in a guaranteed minimum of singular values. A specific combination of the three resulting singular value matrices is then performed for watermark integration. The proposed approach ensures data integrity, patient confidentiality when sharing data, and robustness to several conventional attacks.

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Section: Related Workmentioning

confidence: 99%

A lossless DWT-SVD domain watermarking for medical information security

Zermi

Khaldi

Kafi

et al. 2021

Multimed Tools Appl

View full text Add to dashboard Cite

show abstract

“…Inspired by Wen et al, a large number of zero-watermarking schemes have appeared, and a substantial portion of them create robust features based on the frequency domain [4,5] or hybrids of image transforms and decomposition [6][7][8][9][10][11][12]. Common image transforms such as the DCT (discrete cosine transform) [9,10], DWT (discrete wavelet transform) [4,7,8,11], DFT (discrete Fourier transform) [4], CAT (cellular automata transform) [5], FrFT (fractional Fourier transform) [6], and CT (contourlet transform) [12] have been applied to zero-watermarking. Utilizing the invariance of the significant values in decompositions such as SVD [6][7][8][9][11][12][13] and QR [10] to further improve the robustness is common in zero-watermarking research.…”

Section: Introductionmentioning

confidence: 99%

A Polar Complex Exponential Transform-Based Zero-Watermarking for Multiple Medical Images with High Discrimination

Wang

Liu

2021

Security and Communication Networks

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Zero-watermarking is one of the solutions for image copyright protection without tampering with images, and thus it is suitable for medical images, which commonly do not allow any distortion. Moment-based zero-watermarking is robust against both image processing and geometric attacks, but the discrimination of watermarks is often ignored by researchers, resulting in the high possibility that host images and fake host images cannot be distinguished by verifier. To this end, this paper proposes a PCET- (polar complex exponential transform-) based zero-watermarking scheme based on the stability of the relationships between moment magnitudes of the same order and stability of the relationships between moment magnitudes of the same repetition, which can handle multiple medical images simultaneously. The scheme first calculates the PCET moment magnitudes for each image in an image group. Then, the magnitudes of the same order and the magnitudes of the same repetition are compared to obtain the content-related features. All the image features are added together to obtain the features for the image group. Finally, the scheme extracts a robust feature vector with the chaos system and takes the bitwise XOR of the robust feature and a scrambled watermark to generate a zero-watermark. The scheme produces robust features with both resistance to various attacks and low similarity among different images. In addition, the one-to-many mapping between magnitudes and robust feature bits reduces the number of moments involved, which not only reduces the computation time but also further improves the robustness. The experimental results show that the proposed scheme meets the performance requirements of zero-watermarking on the robustness, discrimination, and capacity, and it outperforms the state-of-the-art methods in terms of robustness, discrimination, and computational time under the same payloads.

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“…With the construction of hospital normalization, digitization is widely applied in the medical field [1]. Modern medical equipment produces lots of digital medical images every day [2,3]. Owing to the rapid development of information technology, intelligent medicine and remote diagnosis are becoming more and more mature [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Zero-Watermarking Algorithm for Medical Image Based on VGG19 Deep Convolution Neural Network

Han

Jia

et al. 2021

Journal of Healthcare Engineering

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Aiming at the security issues in the storage and transmission of medical images in the medical information system, combined with the special requirements of medical images for the protection of lesion areas, this paper proposes a robust zero-watermarking algorithm for medical images’ security based on VGG19. First, the pretrained VGG19 is used to extract deep feature maps of medical images, which are fused into the feature image. Second, the feature image is transformed by Fourier transform, and low-frequency coefficients of the Fourier transform are selected to construct the feature matrix of the medical image. Then, based on the low-frequency part of the feature matrix of the medical image, the mean-perceptual hashing algorithm is used to achieve a set of 64-bit binary perceptual hashing values, which can effectively resist local nonlinear geometric attacks. Finally, the algorithm adopts a watermarking image after scrambling and the 64-bit binary perceptual hashing value to obtain robust zero-watermarking. At the same time, the proposed algorithm utilizes Hermite chaotic neural network to scramble the watermarking image for secondary protection, which enhances the security of the algorithm. Compared with the existing related works, the proposed algorithm is simple to implement and can effectively resist local nonlinear geometric attacks, with good robustness, security, and invisibility.

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Lossless medical image watermarking method based on significant difference of cellular automata transform coefficient

Cited by 46 publications

References 21 publications

A lossless DWT-SVD domain watermarking for medical information security

A lossless DWT-SVD domain watermarking for medical information security

A Polar Complex Exponential Transform-Based Zero-Watermarking for Multiple Medical Images with High Discrimination

Zero-Watermarking Algorithm for Medical Image Based on VGG19 Deep Convolution Neural Network

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