Robust Zero-watermarking Algorithm Research Articles

Robust zero-watermarking algorithm for multi-medical images based on FFST-Schur and Tent mapping

Aiming at issues such as the difficulty of centralized protection of multiple medical images and the high storage cost caused by repetitive operations, we proposed a robust zero-watermarking algorithm for multiple medical images based on fast finite Shearlet transform (FFST)-Schur and Tent mapping. First, multiple medical images were normalized, and the processed images were fused using a gray-weighted average fusion method for image fusion. Second, an FFST is applied to the fused image to divide the low-frequency sub-bands into blocks of the same size and perform Schur decomposition, which generates a feature image using the magnitude between the largest eigenvalue of each block and the overall average value. Finally, an image encryption algorithm based on Tent mapping is proposed to encrypt the logo image, and the encrypted image and feature image are then subjected to an exclusive OR operation to produce a zero-watermarking image. Experimental results demonstrate that the proposed approach effectively safeguards against standard image processing and geometric and combinatorial attacks. The average performance of the anti-attack algorithm was improved by approximately 3.2 % compared to similar algorithms, indicating the superior robustness of the proposed algorithm.

Retracted: Robust Zero Watermarking Algorithm for Medical Images Based on Zernike-DCT

Retracted: Robust Zero Watermarking Algorithm for Medical Images Based on Zernike-DCT

Robust Zero Watermarking Algorithm for Medical Images Based on Improved NasNet-Mobile and DCT

In the continuous progress of mobile internet technology, medical image processing technology is also always being upgraded and improved. In this field, digital watermarking technology is significant and provides a strong guarantee for medical image information security. This paper offers a robustness zero watermarking strategy for medical pictures based on an Improved NasNet-Mobile convolutional neural network and the discrete cosine transform (DCT) to address the lack of robustness of existing medical image watermarking algorithms. First, the structure of the pre-training network NasNet-Mobile is adjusted by using a fully connected layer with 128 output and a regression layer instead of the original Softmax layer and classification layer, thus generating a regression network with 128 output, whereby the 128 features are extracted from the medical images using the NasNet-Mobile network with migration learning. Migration learning is then performed on the modified NasNet-Mobile network to obtain the trained network, which is then used to extract medical image features, and finally the extracted image features are subjected to DCT transform to extract low frequency data, and the perceptual hashing algorithm processes the extracted data to obtain a 32-bit binary feature vector. Before performing the watermark embedding, the watermark data is encrypted using the chaos mapping algorithm to increase data security. Next, the zero watermarking technique is used to allow the algorithm to embed and extract the watermark without changing the information contained in the medical image. The experimental findings demonstrate the algorithm’s strong resistance to both conventional and geometric assaults. The algorithm offers some practical application value in the realm of medicine when compared to other approaches.

Robust zero-watermarking algorithm for diffusion-weighted images based on multiscale feature fusion

Robust zero-watermarking algorithm for diffusion-weighted images based on multiscale feature fusion

Robust medical zero‐watermarking algorithm based on Residual‐DenseNet

To solve the problem of poor robustness of existing traditional DCT-based medical image watermarking algorithms under geometric attacks, a novel deep learning-based robust zero-watermarking algorithm for medical images is proposed. A Residual-DenseNet is designed, which took low-frequency features after discrete cosine transformation of medical images as labels and applied skip connections and a new objective function to strengthen and extract high-level semantic features that can effectively distinguish different medical images and binarise them to get robust hash vectors. Then, these hash vectors are bound with the chaotically encrypted watermark to generate the corresponding keys to complete the generation of watermark. The proposed algorithm neither modified the original medical image in the watermark generation stage nor required the original medical image in the watermark extraction stage. Moreover, the proposed algorithm is also suitable for multiple watermarks. Experimental results show that the proposed algorithm has good robust performance under both conventional and geometric attacks.

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

With the rapid development of smart medical care, copyright security for medical images is becoming increasingly important. To improve medical images storage and transmission safety, this paper proposes a robust zero-watermarking algorithm for medical images by fusing Dual-Tree Complex Wavelet Transform (DTCWT), Hessenberg decomposition, and Multi-level Discrete Cosine Transform (MDCT). First, the low-frequency sub-band of the medical image is obtained through the DTCWT and MDCT. Then Hessenberg decomposition is used to construct the visual feature vector. Meanwhile, the encryption of the watermarking image by combining cryptographic algorithms, third-party concepts, and chaotic sequences enhances the algorithm’s security. In the proposed algorithm, zero-watermarking technology is utilized to assure the medical images’ completeness. Compared with the existing algorithms, the proposed algorithm has good robustness and invisibility and can efficiently extract the watermarking image and resist different attacks.

Robust zero-watermarking algorithm for medical images based on SIFT and Bandelet-DCT

Robust zero-watermarking algorithm for medical images based on SIFT and Bandelet-DCT

Robust zero-watermarking algorithm for medical images based on <em>K</em>-means and DCT

Robust zero-watermarking algorithm for medical images based on <em>K</em>-means and DCT

Robust zero-watermarking algorithm for medical images based on Hadamard-DWT-DCT

Robust zero-watermarking algorithm for medical images based on Hadamard-DWT-DCT

Robust Zero Watermarking Algorithm for Medical Images Based on Zernike-DCT

Digital medical system not only facilitates the storage and transmission of medical information but also brings information security problems. Aiming at the security of medical images, a robust zero watermarking algorithm for medical images based on Zernike-DCT is proposed. The algorithm first uses a chaotic logic sequence to preprocess and encrypt the watermark, then performs edge detection and Zernike moment processing on the original medical image to get the accurate edge points, and then performs discrete cosine transform (DCT) on them to get the feature vector. Finally, it combines perceptual Hash and zero watermark technology to generate the key to complete the watermark embedding and extraction. The algorithm has good robustness to conventional and geometric attacks, strong antinoise ability, high positioning accuracy, and processing efficiency and is superior to the classical edge detection algorithm in extraction effect. It is a stable and reliable image edge detection algorithm.

A novel robust zero-watermarking algorithm for audio based on sparse representation

Behind the prevalence of multimedia technology, digital copyright disputes are becoming increasingly serious. The digital watermarking prevention technique against the copyright infringement needs to be improved urgently. Among the proposed technologies, zero-watermarking has been favored recently. In order to improve the robustness of the zero-watermarking, a novel robust audio zerowatermarking method based on sparse representation is proposed. The proposed scheme is mainly based on the K-singular value decomposition (K-SVD) algorithm to construct an optimal over complete dictionary from the background audio signal. After that, the orthogonal matching pursuit (OMP) algorithm is used to calculate the sparse coefficient of the segmented test audio and generate the corresponding sparse coefficient matrix. Then, the mean value of absolute sparse coefficients in the sparse matrix of segmented speech is calculated and selected, and then comparing the mean absolute coefficient of segmented speech with the average value of the selected coefficients to realize the embedding of zero-watermarking. Experimental results show that the proposed audio zero-watermarking algorithm based on sparse representation performs effectively in resisting various common attacks. Compared with the baseline works, the proposed method has better robustness.

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

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.

Novel quaternion polar complex exponential transform and its application in color image zero-watermarking

The robust watermarking method embeds the information in the image to protect the copyright. The embedding process will destroy the original image and cannot be effectively applied to the images used in the medicine and military with high integrity demand. A robust zero-watermarking algorithm for lossless copyright protection of color images without changing the original image was proposed in this paper. A novel quaternion polar complex exponential transform (NoQPCET) was firstly constructed with effectively improved performance; then the NoQPCET and chaotic system based robust color image zero-watermarking algorithm was suggested for lossless copyright protection of color images. In the proposed algorithm, the robustness against geometric attacks was enhanced due to the excellent geometric invariance of NoQPCET, and the security was also improved because of the initial value sensitivity of the chaotic system. Experimental results showed that this algorithm had very strong robustness against both geometric attacks and common image processing attacks, and was superior to other algorithms.

A novel robust zero-watermarking algorithm for medical images

A novel robust zero-watermarking algorithm for medical images

A robust zero-watermarking algorithm for lossless copyright protection of medical images

The continuous orthogonal moments of images have become a main tool used in zero-watermarking algorithms in recent years. However, there is a problem that should not be neglected for existing zero-watermarking algorithms, which is known as numerical instability. Because the continuous orthogonal moments used in existing zero-watermarking algorithms are integer-order moments, this problem affects the computational accuracy of such moments and thus affects the performance of zero-watermarking algorithms. Therefore, in this paper, the integer-order radial harmonic Fourier moments (IoRHFMs) are firstly extended to fractional-order radial harmonic Fourier moments (FoRHFMs) to effectively mitigate the problem of numerical instability and improve the computational accuracy of IoRHFMs, and then a FoRHFM-based zero-watermarking algorithm for medical images is proposed to achieve lossless copyright protection of medical images. The experimental results show that this algorithm is highly robust to geometric attacks and common attacks, and is superior to IoRHFM-based and other zero-watermarking algorithms.

A robust zero-watermarking algorithm for color image based on tensor mode expansion

Digital zero-watermarking technology is an effective measure to protect image copyright, and many algorithms have been proposed based on zero-watermark. However, most of the existing zero-watermarking algorithms are designed for grayscale images. In this paper, a new zero-watermarking algorithm for color image based on tensor mode expansion is proposed. In the proposed scheme, four images of R, G, B, and gray are firstly generated from the original image. Then, the four images are appropriately combined to construct two three-dimensional tensors, and tensor expansion is performed on the obtained tensors. By performing singular value decomposition (SVD) and discrete cosine transform (DCT) on the expanded data, a feature image containing the main information of the host image can be generated. Finally, the feature image is fused with the specified logo image to obtain a zero-watermark image. Experimental results show that the proposed algorithm is robust to noise attacks and common image processing attacks, and better performances compared with some existing algorithms are also achieved. In addition, since the proposed algorithm is based on color images, it can make full use of all aspects of color images.

基于SVM的多通道鲁棒图像零水印算法

基于SVM的多通道鲁棒图像零水印算法

Robust Watermarking Algorithm for Medical Volume Data in Internet of Medical Things

The advancement of 5G technology, big data and cloud storage has promoted the rapid development of the Internet of Medical Things (IoMT). Based on the strict security requirements and high level of accuracy required for disease diagnosis and pathological analysis, 3D medical volume data have been created in large numbers. The IoMT facilitates the rapid transfer of medical information and also makes the protection of pathological information and privacy information of patients increasingly prominent. To solve the security problem, a robust zero-watermarking algorithm based on 3D hyperchaos and 3D dual-tree complex wavelet transform is proposed according to the selected feature of medical volume data. The feature combines human visual features with improved perceptual hashing techniques. It is a robust and efficient binary sequence. When implementing the proposed algorithm, the watermark is first scrambled with 3D hyperchaos to enhance security. Then, 3D DTCWT-DCT transformation is applied to medical volume data, and the low-frequency coefficients that can represent the features are selected and binarized to generate the secret key to complete the watermark embedding and extraction. Zero embedding and blind extraction ensure that the original medical volume data is not altered in any form, which meets the special requirements for diagnosis. Simulation results show that the algorithm is robust and can effectively resist common attacks and geometric attacks. It used fewer robust features to effectively bound medical volume data and watermark information, saved bandwidth, and satisfied the security of transmission and storage of medical volume data in the Internet of medical things. In particular, compared with state-of-the-art technology, the proposed algorithm improves the average NC value by 46.67% under geometric attacks.

Efficient copyright protection for three CT images based on quaternion polar harmonic Fourier moments

The current zero-watermarking scheme mainly offers the copyright protection to one image. Although the scheme can be used for many images repeatedly, the repeat operation requires a lot of time and large storage space. Using the quaternion polar harmonic Fourier moments (QPHFM), the paper considers three CT images as a whole to construct the key image and proposes a robust zero-watermarking algorithm offering the copyright protection to three CT images simultaneously, improving the efficiency of watermarking system and saving the storage space. The algorithm first considers the three CT images as three imaginary parts of an array of pure quaternion and calculates their QPHFM and then constructs feature image using the amplitude of QPHFM, and finally makes the chaotic scrambling to the feature image and then the XOR operation to the feature image and watermark image to generate key image. Experiment results prove the proposed scheme can resist common image processing attacks and geometric attacks effectively and thus can be well applied to the copyright protection of three images.

Robust zero-watermarking algorithm based on polar complex exponential transform and logistic mapping

This paper introduces a new zero-watermarking algorithm based on polar complex exponential transform (PCET) and logistic mapping. This algorithm takes advantage of the geometric invariance of PCET to improve the robustness of the algorithm against geometric attacks, and the logistic mapping’s sensitivity to initial values to improve the security of the algorithm. First, the algorithm computes the PCET of the original grayscale image. Then it randomly selects PCET coefficients based on logistic mapping, and computes their magnitudes to obtain a binary feature image. Finally, it performs an exclusive-or operation between the binary feature image and the scrambled logo image to obtain the zero-watermark image. At the stage of copyright verification, the image copyright can be determined by performing the exclusive-or operation between the feature image and the verification image and comparing the resulting image to the original logo image. Experimental results show that this algorithm has excellent robustness against geometric attacks and common image processing attacks and better performance compared to other zero-watermarking algorithms.