A Robust Zero-Watermarking Algorithm for Medical Images Using Curvelet-Dct and RSA Pseudo-random Sequences

When medical images transmitted and stored in hospitals, it require strict security, confidentiality and integrity. However, the transmission of wireless and wired networks has made the medical information vulnerable to attacks like tampering, hacking etc. And the ROI of medical image is unable to tolerate significant changes. In order to dealing these problems, we have proposed an algorithm that introducing the digital watermarking technology to increase the security of medical images. The scheme uses a part of sign sequence of DCT coefficients as the feature vector of images. It can avoid the sophisticated process of finding the Region of Interest (ROI) of medical images. At the same time, the watermarking image is encrypted by Logistic Map to enhance its confidentiality. The experimental results show that the scheme has strong robustness against common attacks and geometric attacks. Moreover, compared with the existing medical watermarking techniques, it can embed much more data, less complexity and make embed multi watermarks realized.

In the information age, network security has gradually become a potentially huge problem, especially in the medical field, it is essential to ensure the accuracy and safety of images, and patient information needs to be included with minimal change. Combined log-polar transform (LPT) and discrete cosine transform (DCT), a novel robust watermarking algorithm for medical images, is proposed. It realized the lossless embedding of patient information into medical images. In the process of feature extraction and watermark embedding, the proposed algorithm reflects the characteristics of LPT, scale invariance and rotation invariance, and retains the advantages of DCT's ability to resist conventional attacks and robustness. As it adopts zero-watermarking embedding technology, it solves the defects caused by the traditional watermark embedding technology to modify the original image data and guaranteed the quality of medical images. The good experimental results show the effectiveness of this algorithm.

Robust color image watermarking algorithm based on synchronization correction with multi-layer perceptron and Cauchy distribution model

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.

An enhanced hybrid image watermarking scheme for security of medical and non-medical images based on DWT and 2-D SVD

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

With the advancement in information technology, digital data stealing and duplication have become easier. Over a trillion bytes of data are generated and shared on social media through the internet in a single day, and the authenticity of digital data is currently a major problem. Cryptography and image watermarking are domains that provide multiple security services, such as authenticity, integrity, and privacy. In this paper, a digital image watermarking technique is proposed that employs the least significant bit (LSB) and canny edge detection method. The proposed method provides better security services and it is computationally less expensive, which is the demand of today’s world. The major contribution of this method is to find suitable places for watermarking embedding and provides additional watermark security by scrambling the watermark image. A digital image is divided into non-overlapping blocks, and the gradient is calculated for each block. Then convolution masks are applied to find the gradient direction and magnitude, and non-maximum suppression is applied. Finally, LSB is used to embed the watermark in the hysteresis step. Furthermore, additional security is provided by scrambling the watermark signal using our chaotic substitution box. The proposed technique is more secure because of LSB’s high payload and watermark embedding feature after a canny edge detection filter. The canny edge gradient direction and magnitude find how many bits will be embedded. To test the performance of the proposed technique, several image processing, and geometrical attacks are performed. The proposed method shows high robustness to image processing and geometrical attacks.

An enhanced time efficient technique for image watermarking using ant colony optimization and light gradient boosting algorithm

With the widespread use of color images, the copyright protection of those images using watermarks is one of the latest research topics. The use of color images as watermarks has advantages over binary and irreplaceable grayscale images. Color images are intuitive, rich, and lively; they have large amounts of copyright protection information and more easily recognized by human vision. To improve the security of watermark information and embedding positions and improve the algorithm's robustness against various attacks, a Quaternion Fourier transform (QFT) based algorithm, based on Arnold transform and chaotic encryption, is proposed in this paper. Geometric algebra (GA) can deal with color images in vector form with each component of RGB handled individually. We used Quaternion, which is a sub-algebra of GA, and effectively handled color image processing by using Fourier transformation. After deriving the calculation process of the QFT with strong security by Arnold scrambling and chaotic encryption, this paper proposes a digital watermarking algorithm that resists geometric attacks by using color images as carriers. The robustness and quality of the proposed watermarking algorithm is tested with different with many statistical measures. Experimental outcomes show that the proposed approach is the best to solve conflict problems between quality and robustness. Also, the proposed approach exhibits worthy robustness against many attacks, such as, conventional attacks, and geometrical attacks.

This paper presents a new digital image watermark algorithm, which based on the second generation wavelet. The algorithm decomposes the host image by multi-resolution wavelet firstly, and then embeds watermark into low frequency part with wavelet transformation, and meanwhile embeds watermark into intermediate frequency part with threshold method. This algorithm has good robustness under some conventional attacks and geometrical attacks. Also it is robust with static image compression.

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.

Sharing medical documents among different specialists in different hospitals has become popular as a result of modern communication technologies. Accordingly, protecting patients’ data and authenticity against any unauthorized access or modification is a must. Watermarking technique is one of the solutions to protect patients’ information against any signal processing or geometric attacks. This paper uses a tamper detection and localization watermarking technique. It embeds a Quick Response (QR) code generated from the patient’s information into a medical image. In the extraction step, it can detect if the QR image is attached or not. The proposed approach detects signal processing and geometric attacks and localizes the tamper resulting from text addition, content removal and copy and paste attacks.

Image fusion is the process of gathering all the important information of multiple images into a single fused image. It is used in many fields like medical, military, remote sensing etc... Image fusion in a medical field is extensively used by physicians for analysis and treatment, as the fused image contains complementary features present in different medical images obtained from imaging devices. The wave atom transform based medical image fusion is proposed as the potential capabilities of wave atoms have been exploded in many applications like image denoising, fingerprint identification, compression etc. Medical image techniques became important tool for medical diagnosis and treatment. Medical images of different modalities such as MRI, CT, PET, SPECT provide information in a limited domain. In medical resources, MRI and CT images contain structural information whereas PET and SPECT images contain functional Information. Thus, single modality does not provide complete and accurate information, both structural as well as functional information is required. So far, many medical image fusion methods have been developed. The easiest way to perform image fusion is to perform weighted average of input images pixel by pixel. But this method produces side effects such as artifacts & contrast reduction. The proposed method is experimented on various medical images and compared with recent state of fusion methods. Results prove that images obtained from proposed method have better clarity and enhanced information and are practically more helpful for better treatment and quick analysis.

A robust and geometric invariant digital watermarking scheme for gray-level images is proposed in this paper. The scheme carries out watermark embedding and extraction based on histogram in DWT domain. For watermark embedding, firstly, the original image is decomposed into the approximation and details sub-bands, of which, the approximation sub-band is used for watermark embedding. Pixels of the approximation subband are grouped into m blocks, each of which has the same number of gray-levels, thus the block histogram is generated; with the block histogram, the percentage of number of pixels in each block is calculated. Then some pixels in a block are moved to form a specific pattern in the gray-level histogram distribution, indicating the watermark. Finally, the embedded approximation sub-band and the other three details sub-bands are constructed into a watermarked image. For watermark extraction, firstly, the watermarked image is also decomposed into the approximation and details sub-bands; then the pixels in the approximation sub-band are grouped into blocks in the similar manner. According to the histogram distribution in each block, the watermark is extracted. Experimental results show that the proposed scheme is highly robust against JPEG compression, geometric attacks and some common signal processing, and it outperforms the existing methods in term of robustness.