3D DWT-DCT and Logistic MAP Based Robust Watermarking for Medical Volume Data

[This retracts the article on p. 131 in vol. 8.].

Three-dimensional medical volume data such as high-resolution map of medical CT, advanced optical images of scanners and MRI has increasingly developed to facilitate medicine diagnosis, the information storage, and the transmission, while it also brings security issues. In this paper, an encryption algorithm is presented, considering the s ecurity of three-dimensional medical volume data. Firstly, the position of the pixels of the medical volume data is scrambled by three-dimensional Arnold and a chaotic sequence is produced by Logistic map. S econdly, the image feature value is extracted as a key into the encryption process, and then a feedback mechanism was set up to change the pixel values. Finally, the definition of encryption performance indicators in the volume data is promoted. Through simulation, the algorithm has a large key space, which is highly sensitive to the initial value of keys. Furthermore, it can resist statistical, brute-force attack and differential attack effectively. The proposed encryption algorithm in this paper has high security and practical value, gi ving inspiration on security issues in the future with the large emergence of the three-dimensional volume data.

This paper proposed a compressed domain algorithm of robust blind watermarking method for medical volume data, which addresses the problems of authentication and protection of personal information. The scheme obtains the visual feature vectors of medical volume data in DCT compressed domain. Through the concept of zero-watermarking, the algorithm for watermarking of volume data that is robust to geometric attacks. The experimental results demonstrate that the proposed algorithm has good invisibility and robustness.

With the development of medical digitization, smart medical care and medical big data technologies, secure storage and sharing of medical data have become the hotspot of research in the field of medical technology. However, Fragmentation of medical data is much more serious, and many data becomes data islands that are difficult to apply to medical research. At the same time, the centralized control of medical data by third-party institutions has security bottlenecks and performance bottlenecks, which also lead to frequent data leakage incidents. To solve this problem, we propose a medical health data sharing scheme based on blockchain and attribute-based encryption. The model implements decentralized, tamper-proof, traceable data sharing based on a distributed blockchain system. And an attribute-based encryption is used to protect the privacy of patient's medical data. The cryptographic method is used to implement fine-grained access control of the data. The model can reduce the cost of data sharing and improve the efficiency of data utilization while protecting the data security. Finally, compared with the existing medical data sharing methods, our model has certain advantages and good scalability in some aspects.

Medical images include confidential and sensitive information about patients. Hence, ensuring the security of these images is a crucial requirement. This paper proposes an efficient and secure medical image encryption‐decryption scheme based on deoxyribonucleic acid (DNA), one‐dimensional chaotic maps (tent and logistic maps), and hash functions (SHA‐256 and MD5). The first part of the proposed scheme is the key generation based on the hash functions of the image and its metadata. The key then is highly related and intensely sensitive to the original image. The second part is the rotation and permutation of the first two MSB bit‐plans of the medical image to reduce its black background that produces redundant DNA encoded sequences. The third part is the DNA encoding‐decoding using dynamically chosen DNA rules for every 2‐bit pixel value through the logistic map. Meanwhile, the confusion‐diffusion is performed using the tent map and XOR operation. Simulation results and security analysis prove the good encryption effects of the proposed scheme compared to the state‐of‐art methods with a correlation of 6.66617e‐7 and a very large key space of 2 624 . Furthermore, the proposed system has a strong ability to resist various common attacks such as chosen/known‐plaintext attacks and cropping/noise attacks.

This paper presents a new technique for copyright protection and content authentication of satellite images. The novelty in the presented approach consists in designing a hybrid Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT) and hash function based digital image watermarking. In this study, two watermarks are embedded in the cover image called robust copyright watermark and fragile authentication watermark. The robust watermark is embedded in hybrid frequency and wavelet domain by applying DWT and DCT respectively. Such two steps embedding allows the proposed approach to provide better imperceptibility in harmony with the human visual system and offers higher robustness against signal processing attacks. Subsequently, the fragile watermark is embedded in a spatial domain using the hash function approach. The proposed hybrid watermarking technique has been tested on DubaiSat-2 images with 1 meter resolution. The experimental results show that the proposed method is robust against JPEG compression, rotation and resizing attacks. In addition, the multi-watermarked image has a good transparency and the fragile watermark is sensitive to any tampering.

Visualization of classification uncertainty of the medical materials is very significant, as different classifications may lead to different visualization results, and different results may cause completely different diagnosis or pre-operative planning decisions, and thus have different consequences to patients. Traditional Direct Volume Rendering (DVR) enables medical experts to visualize the classification uncertainty by random adjustment of the transfer function. However, three main problems exist by using this method: first, the resulting renderings do not indicate any quantitative information about the classification uncertainty of different materials; second, the resulting renderings are randomly revealed by random adjustment of the TF; third, both classification task and optical property assignment task are mixed together in one step. These problems may make this method (1) fail to enable medical experts to make accurate diagnosis or pre-operative planning decision; (2) unable to provide medical experts with a clear concept of how medical volume data are classified. To address these problems, we proposed a probabilistic slider system, which compared to the traditional DVR, enables medical experts to make more accurate diagnosis or pre-operative planning decision, and have a clearer concept of how the medical volume data being classified.

In this paper, a new robust and secure video watermarking scheme based on discrete Wavelet Transform (DWT), singular value decomposition (SVD), and Laplacian pyramid is proposed for copyright protection applications. The main contributions of this work are: 1) Increasing watermark robustness towards common signal processing attacks by separating the watermark image into a high-frequency image and a low-frequency image using laplacian pyramid. Then, the singular values of each of the low-frequency watermark image and the high-frequency watermark image are hidden in two different DWT subbands of the original video frame. Separating the watermark and hiding its two parts in two different subbands of the DWT of the video frame makes it robust towards noise, filtering, and compression attacks. 2) Improving the quality of the extracted watermarks via correcting the received watermarked videos using SURF points when those videos are geometrically attacked, mainly by rotation, scaling, and shear attacks. 3) Increasing security and avoiding false positive problems (FPP) associated with SVD by using perceptual image hashing in the proposed scheme. Through experimental results and comparisons to other techniques, the proposed scheme has shown very high imperceptibility for the tested watermarked videos in terms of peak signal to noise ratio (PSNR). Also, the proposed scheme has demonstrated high watermark robustness towards various types of attacks, and an improvement in the quality of the extracted watermarks from 4 to 20 dB compared to other techniques.

Medical image copyright protection is becoming increasingly relevant as medical images are used more frequently in medical networks and institutions. The traditional embedded watermarking system is inappropriate for medical images since it degrades the original images’ quality. Furthermore, medical-colored image watermarking options are constrained since most medical watermarking systems are built for gray-scale images. This paper proposes a zero-watermarking scheme for medical color image copyright protection based on a chaotic system and Resnet50, which is a convolutional neural network method. The network Resnet50 is used to extract features from the color medical image, and then a logistic Gaussian map is used to scramble these features and scramble the binary image. Finally, an exclusive OR operation is performed (scrambled binary image, scrambled features for the medical color image) to form a zero watermarking. The experimental result proves that our scheme is effective and robust to geometric and common image processing attacks. The BER values of the extracted watermarks are below 0.0039, and the NCC values are above 0.9942, while the average PSNR values of the attacked images are 29.0056 dB. Also, it is superior to other zero-watermark schemes for medical images in terms of robustness to conventional image processing and geometric attacks. Furthermore, the experimental results show that the Resnet50 model outperforms other models in terms of reducing the mean squared errors of the features between the attacked and original image.

This paper presents an intelligent texture anti-counterfeiting method based on human visual characteristics and DFT. By using a part of sign sequence of DFT coefficients as the feature vector of texture images, we extract a visual feature vector and then create a database of texture images' feature vectors. After comparing the Normalized Cross-correlation (NC) between the feature vectors, we achieve the purpose of automatic identification. The experimental results show that the proposed algorithm has strong robustness against common and geometric attacks.

Digital watermarking is an efficient technique for copyright protection in the current digital and network era. In this paper, a novel robust watermarking scheme is proposed based on singular value decomposition (SVD), Arnold scrambling (AS), scale invariant feature transform (SIFT) and majority voting mechanism (MVM). The watermark is embedded into each image block for three times in a novel way to enhance the robustness of the proposed watermarking scheme, while Arnold scrambling is utilized to improve the security of the proposed method. During the extraction procedure, SIFT feature points are used to detect and correct possibly geometrical attacks, and majority voting mechanism is performed to enhance the accuracy of the extracted watermark. Our analyses and experimental results demonstrate that the proposed watermarking scheme is not only robust to a wide range of common signal processing attacks (such as noise, compression and filtering attacks), but also has favorable resistance to geometrical attacks.

Currently, the medical information system is meeting many difficulties when conveying information via the network, such as leaking out the patients’ information, falsifying medical records, and destroying the structure of medical images etc. For these questions, the new algorithm with ultra-strong robustness is proposed, its innovation lies in perfectly combining digital watermarking technology, which is the domain technology in information security area, the emerging perceptual Hash technology, the classical threedimensional reconstruction technology, information encryption technology, and “the third party” certification together. Firstly, it is important to preprocess the original watermarking by means of Arnold scrambling; secondly, to obtain the needed information by 3D-DFT, we utilize perceptual Hash function to stratify and reduce the dimensions, as well as compress the medical volume data into 8  8, then the visual feature vector will be gained after reconstructing the three-dimensional image, which is the result of 3D-IDFT; ultimately, employing the bit operation of Hash function between the encrypted watermarking and visual feature vector, we can realize the embedding process. The experiments indicate that the approach is suitable for medical application because of its robustness. KeywordsPerceptual Hash; 3D reconstruction; Visual feature vector; Arnold scrambling;3D-DFT

Aiming at the problem of digital image encryption, an image encryption algorithm is proposed. The algorithm is divided into preparation stage and permutation diffusion stage. In the preparation stage, Arnold algorithm is improved and combined with Logistic chaotic map to generate the necessary sequence for the next stage. In the permutation diffusion stage, the final encrypted image is obtained by permutation, positive XOR operation, shift operation, S-box substitution and reverse XOR operation. The experimental results show that the algorithm can effectively resist the common attacks. For the 128 × 128 Lena gray image, the resistance performance number of pixel change rate (NPCR) is 99.6090, unified average changing intensity (UACI) is 33.4316. In addition, the time consumption of the algorithm is about 0.0055 s, and the experiments show that the algorithm has high security and real-time performance.

The existing medical image privacy solutions cannot completely solve the security problems created by applying the metaverse healthcare system. A robust zero-watermarking scheme based on the Swin Transformer is proposed in this article to improve the security of medical images in the metaverse healthcare system. This scheme uses a pretrained Swin Transformer to extract deep features from the original medical images with a good generalization performance and multiscale, and binary feature vectors are generated by using the mean hashing algorithm. Then, the logistic chaotic encryption algorithm boosts the security of the watermarking image by encrypting it. Finally, an encrypted watermarking image is XORed with the binary feature vector to create a zero-watermarking, and the validity of the proposed scheme is verified through experimentation. According to the results of the experiments, the proposed scheme has excellent robustness to common attacks and geometric attacks, and implements privacy protections for medical image security transmissions in the metaverse. The research results provide a reference for the data security and privacy protection of the metaverse healthcare system.

In recent years, with the development of the image processing technology, the protection to the authenticity and integrity of digital images has always been a topic of great concern. A blind image authentication technology with high tamper detection accuracy under different common attacks is urgently needed. In this paper, an improved fragile watermarking based on local binary pattern (LBP) for blind tamper detection in images is proposed. In this method, the binary watermark is generated by LBP operator, which is often used in texture analysis and pattern recognition. In order to improve the security of the proposed method, Arnold transform and logistic map are used to scramble the watermark. Then the watermark is inserted into the least significant bit (LSB) of original pixels. Since the watermark is constructed from the image itself, no original image is needed in tamper detection. The LBP map of watermarked image is compared with the extracted watermark to determine whether it is tampered or not. Compared with other state-of-the-art algorithms, experimental results show that the proposed scheme achieves good performance in tamper detection and localization under various attacks.