In this paper, a blind image watermarking scheme based on discrete wavelet transform (DWT) and singular value decomposition (SVD) is proposed. In this scheme, DWT is applied on ROI (region of interest) of the medical image to get different frequency subbands of its wavelet decomposition. On the low frequency subband LL of the ROI, block-SVD is applied to get different singular matrices. A pair of elements with similar values is identified from the left singular value matrix of these selected blocks. The values of these pairs are modified using certain threshold to embed a bit of watermark content. Appropriate threshold is chosen to achieve the imperceptibility and robustness of medical image and watermark contents respectively. For authentication and identification of original medical image, one watermark image (logo) and other text watermark have been used. The watermark image provides authentication whereas the text data represents electronic patient record (EPR) for identification. At receiving end, blind recovery of both watermark contents is performed by a similar comparison scheme used during the embedding process. The proposed algorithm is applied on various groups of medical images like X-ray, CT scan and mammography. This scheme offers better visibility of watermarked image and recovery of watermark content due to DWT-SVD combination. Moreover, use of Hamming error correcting code (ECC) on EPR text bits reduces the BER and thus provides better recovery of EPR. The performance of proposed algorithm with EPR data coding by Hamming code is compared with the BCH error correcting code and it is found that later one perform better. A result analysis shows that imperceptibility of watermarked image is better as PSNR is above 43 dB and WPSNR is above 52 dB for all set of images. In addition, robustness of the scheme is better than existing scheme for similar set of medical images in terms of normalized correlation coefficient (NCC) and bit-error-rate (BER). An analysis is also carried out to verify the performance of the proposed scheme for different size of watermark contents (image and EPR data). It is observed from analysis that the proposed scheme is also appropriate for watermarking of color image. Using proposed scheme, watermark contents are extracted successfully under various noise attacks like JPEG compression, filtering, Gaussian noise, Salt and pepper noise, cropping, filtering and rotation. Performance comparison of proposed scheme with existing schemes shows proposed scheme has better robustness against different types of attacks. Moreover, the proposed scheme is also robust under set of benchmark attacks known as checkmark attacks.

A blind medical image watermarking: DWT-SVD based robust and secure approach for telemedicine applications

This paper presents a blind dual watermarking mechanism for digital color images in which invisible robust watermarks are embedded for copyright protection and fragile watermarks are embedded for image authentication. For the purpose of copyright protection, the first watermark is embedded using the discrete wavelet transform in YCbCr color space, and it can be extracted blindly without access to the host image. However, fragile watermarking is based on an improved least significant bits’ replacement approach in RGB components for image authentication. The authenticity and integrity of a suspicious image can be verified blindly without the host image and the original watermark. The combination of robust and fragile watermarking makes the proposed mechanism suitable for protecting valuable original images. The experimental results indicated that the proposed watermarking mechanism can withstand various processing attacks and accurately locate the tampered area of an image.

Blind Dual Watermarking for Color Images’ Authentication and Copyright Protection

An image watermarking scheme in wavelet domain with optimized compensation of singular value decomposition via artificial bee colony

This survey presents a brief discussion of different aspects of digital image watermarking. Included in the present discussion are these general concepts: major characteristics of digital watermark, novel and recent applications of watermarking, different kinds of watermarking techniques and common watermark embedding and extraction process. In addition, recent state-of-art watermarking techniques, potential issues and available solutions are discussed in brief. Further, the performance summary of the various state-of-art watermarking techniques is presented in tabular format. This survey contribution will be useful for the researchers to implement efficient watermarking techniques for secure e-governance applications.

A recent survey on image watermarking techniques and its application in e-governance

In this paper, a novel image watermarking method is proposed which is based on discrete wave transformation (DWT), Hessenberg decomposition (HD), and singular value decomposition (SVD). First, in the embedding process, the host image is decomposed into a number of sub-bands through multi-level DWT, and the resulting coefficients of which are then used as the input for HD. The watermark is operated on the SVD at the same time. The watermark is finally embedded into the host image by the scaling factor. Fruit fly optimization algorithm, one of the natural-inspired optimization algorithms is devoted to find the scaling factor through the proposed objective evaluation function. The proposed method is compared to other research works under various spoof attacks, such as the filter, noise, JPEG compression, JPEG2000 compression, and sharpening attacks. The experimental results show that the proposed image watermarking method has a good trade-off between robustness and invisibility even for the watermarks with multiple sizes.

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An Optimized Image Watermarking Method Based on HD and SVD in DWT Domain

A blind dual color images watermarking based on singular value decomposition

In this paper, a novel blind image watermarking scheme based on QR decomposition is proposed to embed color watermark image into color host image, which is significantly different from using the binary or gray image as watermark. When embedding watermark, the 24-bits color host image with size of 512?×?512 is divided into non-overlapping 4?×?4 pixel blocks and each pixel block is decomposed by QR. Then, according to the watermark information and the relation between the second row first column coefficient and the third row first column coefficient in the unitary matrix Q, the 24-bits color watermark image with size of 32?×?32 is embedded into the color host image. In addition, the new element compensatory method is used in the upper-triangle matrix R for reducing the visible distortion. When extracting watermark, only the watermarked image is needed. Compared with other SVD-based methods, the proposed method does not have the false-positive detection problem and has lower computational complexity, that is, the average running time of the proposed method only needs 1.481403 s. The experimental results show that the proposed method is robust against most common attacks including JPEG compression, JPEG 2000 compression, low-pass filtering, cropping, adding noise, blurring, rotation, scaling and sharpening et al. Compared with some related existing methods, the proposed algorithm has stronger robustness and better invisibility.

A blind double color image watermarking algorithm based on QR decomposition

Survey of deep learning techniques for disease prediction based on omics data

Computational prediction of the RBP bound sites using features learned from existing annotation knowledge is an effective method because high-throughput experiments are complex, expensive and time-consuming. Many methods have been proposed to predict RNA-protein binding sites. However, the partial information of RNA sequence is not fully used. In this study, we propose multiple convolutional neural networks (MCNN) method, which predicts RNA-protein binding sites by integrating multiple convolutional neural networks constructed by RNA sequence information extracted from windows with different lengths. First, MCNN trains multiple CNNs base on RNA sequences extracted by different window lengths. Second, MCNN can extract more binding patterns of RBPs by combining these trained multiple CNNs previously. Third, MCNN only uses RNA base sequence information for RNA-protein binding sites prediction, which extracts sequence binding features and predicts the result with same architecture. This avoids the information loss of feature extraction step. Our proposed MCNN demonstrates a competitive performance comparing with other methods on a large-scale dataset derived from CLIP-seq, which is an effective method for RNA-protein binding sites prediction. The source code of our proposed MCNN method can be found in https://github.com/biomg/MCNN.

MCNN: Multiple Convolutional Neural Networks for RNA-Protein Binding Sites Prediction

RNA‐binding proteins (RBPs) play significant roles in many biological life activities, many algorithms and tools are proposed to predict RBPs for researching biological mechanisms of RNA‐protein binding sites. Deep learning algorithms based on traditional machine learning get better result for predicting RBPs. Recently, deep learning method fused with attention mechanism has attracted huge attention in many fields and gets competitive result. Thus, attention mechanism module may also improve model performance for predicting RNA‐protein binding sites. In this study, we propose convolutional residual multi‐head self‐attention network (CRMSNet) that combines convolutional neural network (CNN), ResNet, and multi‐head self‐attention blocks to find RBPs for RNA sequence. First, CRMSNet incorporates convolutional neural networks, recurrent neural networks, and multi‐head self‐attention block. Second, CRMSNet can draw binding motif pictures from the convolutional layer parameters. Third, attention mechanism module combines the local and global RNA sequence information for capturing long sequence feature. CRMSNet gets competitive AUC (area under the receiver operating characteristic [ROC] curve) result in a large‐scale dataset RBP‐24. And CRMSNet experiment result is also compared with other state‐of‐the‐art methods. The source code of our proposed CRMSNet method can be found in https://github.com/biomg/CRMSNet.

Hailin Zou

Papers

A blind dual color images watermarking based on singular value decomposition

A blind double color image watermarking algorithm based on QR decomposition

Survey of deep learning techniques for disease prediction based on omics data

MCNN: Multiple Convolutional Neural Networks for RNA-Protein Binding Sites Prediction

CRMSNet: A deep learning model that uses convolution and residual multi‐head self‐attention block to predict RBPs for RNA sequence