A Watermarking Technique for Biomedical Images Using SMQT, Otsu, and Fuzzy C-Means

Shoron, Shaekh Hasan; Islam, Monamy; Uddin, Jia; Shon, Dongkoo; Im, Ki Chun; Park, Jeong-Ho; Lim, Dong-Sun; Jang, Byungtae; Kim, Jong‐Myon

doi:10.3390/electronics8090975

Cited by 8 publications

(8 citation statements)

References 15 publications

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“…Taking into account formula (9) in factorization (8) we obtain the third variant of optimized two-dimensional operator with the data flow diagram of the form depicted in Fig. 6.…”

Section: The Data Flow Diagram For the First Variant Of Factorization...mentioning

confidence: 99%

“…Discrete two-dimensional orthogonal wavelet transforms find wide applications in image compression, image processing, classification of images, image watermarking, industrial informatics, etc. (e.g., see [1][2][3][4][5][6][7][8][9]). The typical way to calculate twodimensional wavelet transforms is to assume their separability and take advantage of row-column approach where onedimensional transformation is applied first to rows (columns) and then to columns (rows) of an input image.…”

Section: Introductionmentioning

confidence: 99%

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Bulletin of the Polish Academy of Sciences: Technical Sciences

Puchala

2022

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Discrete two-dimensional orthogonal wavelet transforms find applications in many areas of analysis and processing of digital images. In a typical scenario the separability of two-dimensional wavelet transforms is assumed and all calculations follow the row-column approach using one-dimensional transforms. For the calculation of one-dimensional transforms the lattice structures, which can be characterized by high computational efficiency and non-redundant parametrization, are often used. In this paper we show that the row-column approach can be excessive in the number of multiplications and rotations. Moreover, we propose the novel approach based on natively two-dimensional base operators which allows for significant reduction in the number of elementary operations, i.e., more than twofold reduction in the number of multiplications and fourfold reduction of rotations. The additional computational costs that arise instead include an increase in the number of additions, and introduction of bit-shift operations. It should be noted, that such operations are significantly less demanding in hardware realizations than multiplications and rotations. The performed experimental analysis proves the practical effectiveness of the proposed approach.

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“…Taking into account formula (9) in factorization (8) we obtain the third variant of optimized two-dimensional operator with the data flow diagram of the form depicted in Fig. 6.…”

Section: The Data Flow Diagram For the First Variant Of Factorization...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bulletin of the Polish Academy of Sciences: Technical Sciences

Puchala

2022

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“…However, this is usually a trade-off problem, and several scholars have devoted themselves to identifying the best solution. Shoron et al [28] proposed a copyright protection mechanism based on DWT and successive mean quantization transform successive mean quantization transform for medical images. Although this scheme achieved satisfactory experiment results, the approaches of malicious attacks that can be resisted do not include most image processing methods.…”

Section: Lossless Watermarking Schemementioning

confidence: 99%

WMNet: A Lossless Watermarking Technique Using Deep Learning for Medical Image Authentication

2021

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Traditional watermarking techniques extract the watermark from a suspected image, allowing the copyright information regarding the image owner to be identified by the naked eye or by similarity estimation methods such as bit error rate and normalized correlation. However, this process should be more objective. In this paper, we implemented a model based on deep learning technology that can accurately identify the watermark copyright, known as WMNet. In the past, when establishing deep learning models, a large amount of training data needed to be collected. While constructing WMNet, we implemented a simulated process to generate a large number of distorted watermarks, and then collected them to form a training dataset. However, not all watermarks in the training dataset could properly provide copyright information. Therefore, according to the set restrictions, we divided the watermarks in the training dataset into two categories; consequently, WMNet could learn and identify the copyright information that the watermarks contained, so as to assist in the copyright verification process. Even if the retrieved watermark information was incomplete, the copyright information it contained could still be interpreted objectively and accurately. The results show that the method proposed by this study is relatively effective.

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“…step 3. Based on the watermark bit information W (i, j), modifying magnitudes M1 and M2 are decided to modify the DC coefficient DC (i, j), as given in the Equations ( 14) and (15).…”

Section: Watermark Insertion Processmentioning

confidence: 99%

“…Frequency domain methods first transform the spatial representation into the frequency domain and then modify the frequency coefficients. Literature also reveals the implementation of various transforms, such as the discrete wavelet transform (DWT), discrete Fourier transform (DFT), quaternion wavelet transform (QWT), discrete fractional Fourier transform (DFrFT), singular value decomposition (SVD), discrete cosine transform (DCT), and their combinations, for image watermarking schemes [8][9][10][11][12][13][14][15][16]. A redistributed invariant discrete wavelet transform (RIDWT) image watermarking technique was introduced by Li et al [17].…”

Section: Introductionmentioning

confidence: 99%

An Optimized Digital Watermarking Scheme Based on Invariant DC Coefficients in Spatial Domain

et al. 2020

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Digital watermarking has become an essential and important tool for copyright protection, authentication, and security of multimedia contents. It is the process of embedding a watermark in the multimedia content and its extraction. Block-based discrete cosine transform (DCT) is a widely used method in digital watermarking. This paper proposes a novel blind image watermarking scheme developed in the spatial domain by quantization of invariant direct current (DC) coefficients. The cover image is redistributed and divided into non-overlapped square blocks and then the DC coefficients invariant to rotation, row and column flip operations, without utilization of the DCT transform, are directly calculated in the spatial domain. Utilizing the quantization parameter and watermark information, the modified DC coefficients and the difference between DC and modified DC coefficients are calculated to directly modify the pixel values to embed watermark bits in the spatial domain instead of the DCT domain. Optimal values of the quantization parameter, which plays a significant role in controlling the tradeoff between robustness and invisibility, are calculated through differential evolution (DE), the optimization algorithm. Experimental results, compared with the latest similar schemes, demonstrate the advantages of the proposed scheme.

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A Watermarking Technique for Biomedical Images Using SMQT, Otsu, and Fuzzy C-Means

Cited by 8 publications

References 15 publications

Bulletin of the Polish Academy of Sciences: Technical Sciences

Bulletin of the Polish Academy of Sciences: Technical Sciences

WMNet: A Lossless Watermarking Technique Using Deep Learning for Medical Image Authentication

An Optimized Digital Watermarking Scheme Based on Invariant DC Coefficients in Spatial Domain

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