Medical Image Watermarking Research Articles

A robust medical image zero-watermarking algorithm using Collatz and Fresnelet Transforms

Zero-watermarking in medical images is an emerging field that focuses on calculating the invisible data (key) using medical imagery to ensure data integrity and authenticity without compromising diagnostic accuracy. This paper introduces a robust zero-watermarking technique leveraging the Collatz and Fresnelet Transforms. The Forward Collatz Transform (FCT) is initially applied to create a secure and encrypted embedding pattern for medical images. Subsequently, the Fresnelet Transform (FT) is employed, offering superior localization and frequency selectivity. From the fresnelet values, we extract two strongest Oriented FAST and Rotated BRIEF (ORB) points to enhance watermark robustness, resulting in a 64-bit perceptual image hash. Our approach adopts a dual-layer security strategy by combining FCT and Cyclic-Shift-Transformation (CST) methods, significantly fortifying the protection of watermark image data. The watermark can be efficiently extracted using the Inverse Collatz Transform (ICT). A comprehensive performance analysis evaluates our system under single, double, and multiple attacks on medical images. Our experiments clearly show that our system outperforms existing methods in medical image watermarking, demonstrating its resilience against various manipulations. This approach can significantly improve data security and reliability in medical imaging applications.

A reversible-zero watermarking scheme for medical images

The paper addresses the issue of ensuring the authenticity and copyright of medical images in telemedicine applications, with a specific emphasis on watermarking methods. While several systems only concentrate on identifying tampering in medical images, others also provide the capacity to restore the tampered regions upon detection. While several authentication techniques in medical imaging have successfully achieved their goals, previous research underscores a notable deficiency: the resilience of these schemes against unintentional attacks has not been sufficiently examined or emphasized in previous research. This indicates the need for further development and investigation in improving the robustness of medical image authentication techniques against unintentional attacks. This research proposes a Reversible-Zero Watermarking approach as a solution to address these problems. The new approach merges the advantages of both the reversible and zero watermarking techniques. This system is comprised of two parts. The first part is a zero-watermarking technique that uses VGG19-based feature extraction and watermark information to establish an ownership share. The second part incorporates this ownership share into the image in a reversible manner using a combination of a discrete wavelet transform, an integer wavelet transform, and a difference expansion. Research findings confirm that the suggested watermarking approach for medical images demonstrates substantial enhancements compared to current methodologies. Research findings indicate that NC values are often around 0.9 for different attacks, whereas BER values are close to 0. It demonstrates exceptional qualities in being imperceptible, distinguishable, and robust. Additionally, the system provides a persistent verification feature that functions independently of disputes or third-party storage, making it the preferred choice in the domain of medical image watermarking.

A DWT-Based Approach with Gradient Analysis for Robust and Blind Medical Image Watermarking

The growing adoption of telemedicine necessitates robust security measures for medical images during transmission. This paper proposes a novel blind watermarking system for medical images that utilizes both image gradients and the Discrete Wavelet Transform (DWT). Image gradients, acting as spatial derivatives, provide a “topological map” of the image, aiding in the identification of areas susceptible to disruption. The DWT, with its multi-resolution analysis, offers a favorable balance between robustness and imperceptibility. The proposed method embeds the watermark within the low–low band (LL) of the DWT-decomposed image, specifically in 3 × 3 block regions selected based on gradient information. The mathematical relationships between the gradient’s direction and magnitude are employed to extract the corresponding blocks and their codes adequately. These codes are then XORed with the watermark and embedded into the chosen blocks using the least significant bit (LSB) technique. Extensive experimentation on a medical image dataset evaluates the system’s performance against some attacks like filtering, noise, and scaling. The results demonstrate the efficacy of the proposed approach in hiding information while ensuring the security and integrity of watermarked medical images.

A Robust and Secure Medical Image Watermarking Algorithm Based on Normalized DCT and Polar-coded UFMC Assisted NOMA Scheme for Telemedicine Applications

A Robust and Secure Medical Image Watermarking Algorithm Based on Normalized DCT and Polar-coded UFMC Assisted NOMA Scheme for Telemedicine Applications

Join security and block watermarking-based evolutionary algorithm and Racah moments for medical imaging

Ensuring the security of medical images containing confidential patient health information has become crucial. This paper presents a multipurpose medical image system based on a join of security and watermarking to achieve high data protection. We propose a block-splitting technique applied to large-scale cover medical images and hidden watermarks. In each subspace, we calculate Racah moments and apply a new evolutionary algorithm to automatically select the best positions for moments to be used for watermark hiding. This selection is based on a set of chromosomes with a specific coding strategy and evolutionary operators. The entire watermarked medical image is transmitted, and the extraction process is applied on the receiving side to recover the hidden watermark. Our method aims to achieve an optimal trade-off between watermarking requirements (imperceptibility and robustness) and enhance data protection by combining the proposed method with the three security services (confidentiality, integrity, and authentication). This hybrid combination uses high-performance encryption algorithms applied to the watermark components: the patient’s fingerprint, Electronic Record Patient, and doctor’s face. Watermarking performance and security services are evaluated using different medical datasets (DICOM, Figshare Brain Tumor, Covid-19, CXR, and MuRa images) in the absence of noise and against local or global noise. A comparative analysis with reliable and recent methods in medical image watermarking shows that our method achieves high performance: imperceptibility with a maximum PSNR of 46 dB, robustness with a maximum NC of 0.98, authentication with a maximum PSNR of 36.2 dB, and integrity with a maximum similarity of 0.97.

An improved robust algorithm for optimisation-based colour medical image watermarking

Recently, significant attention has been focused on copyright protection of medical images, causing watermarking to become a prevalent research topic. In this paper, we propose an optimisation-based robust watermarking algorithm for the copyright protection of colour medical images. Initially, we decompose a host medical image by lifting wavelet transform (LWT), followed by discrete cosine transform (DCT) into transformed coefficients, and then embed multiple watermarks into the transformed host image by using hybrid optimisation algorithms. To further improve security and reduce the channel noise distortion, we then apply hashing and Hamming code to image and text mark, respectively, before the embedding process. Finally, we utilise 2D chaotic map to encrypt the marked media, aiming to achieve enhanced security and confidentiality. The experimental outcome shows satisfactory invisibility, watermark payload and robustness against image-processing attacks. Furthermore, through numerous comparative experiments with nine state-of-the-art algorithms on two standard datasets, the effectiveness of the proposed algorithm is demonstrated.

A Reversible Medical Image Watermarking Scheme for Advanced Image Tampering Detection

A Reversible Medical Image Watermarking Scheme for Advanced Image Tampering Detection

Fractional-order heterogeneous memristive Rulkov neuronal network and its medical image watermarking application

This article proposes a novel fractional heterogeneous neural network by coupling a Rulkov neuron with a Hopfield neural network (FRHNN), utilizing memristors for emulating neural synapses. The study firstly demonstrates the coexistence of multiple firing patterns through phase diagrams, Lyapunov exponents (LEs), and bifurcation diagrams. Secondly, the parameter related firing behaviors are described through two-parameter bifurcation diagrams. Subsequently, local attraction basins reveal multi-stability phenomena related to initial values. Moreover, the proposed model is implemented on a microcomputer-based ARM platform, and the experimental results correspond to the numerical simulations. Finally, the article explores the application of digital watermarking for medical images, illustrating its features of excellent imperceptibility, extensive key space, and robustness against attacks including noise and cropping.

MIWET: Medical image watermarking using encryption and fusion technique

Digital augmentation of contemporary healthcare has directed the expansion of ICT-based systems. At this stage, a variety of telemedical support has been implemented to ensure the provision of medical utilities. Data Privacy, reliability, and credibility are the three security prerequisites that demand constant monitoring and corrective measures. Watermarking and cryptographic techniques offer a potentially viable solution for effectively addressing the aforementioned challenges. This article presents a robust and secure watermarking procedure that effectively safeguards the privacy and accuracy of digital medical data. This work uses a hybridization of NSST and DTCWT to implement a multimodality image fusion method that generates the watermark image. Further, a combination of three transforms namely, NSST, BEMD, and MSVD is used to conceal the generated mark in the carrier image. Also, the weighting factor is computed using HOG-based optimization to achieve a balance between visual quality and robustness. Finally, the marked carrier image is encrypted with a suitable encryption technique for better security. The proposed framework’s versatility, embedding capacity, robustness, and imperceptibility are evaluated, with the results justifying its efficacy. The study demonstrates a significant improvement of 47.65 % over the baseline, thus highlighting the framework’s superiority.

Robust zero‐watermarking algorithm based on discrete wavelet transform and daisy descriptors for encrypted medical image

AbstractIn the intricate network environment, the secure transmission of medical images faces challenges such as information leakage and malicious tampering, significantly impacting the accuracy of disease diagnoses by medical professionals. To address this problem, the authors propose a robust feature watermarking algorithm for encrypted medical images based on multi‐stage discrete wavelet transform (DWT), Daisy descriptor, and discrete cosine transform (DCT). The algorithm initially encrypts the original medical image through DWT‐DCT and Logistic mapping. Subsequently, a 3‐stage DWT transformation is applied to the encrypted medical image, with the centre point of the LL3 sub‐band within its low‐frequency component serving as the sampling point. The Daisy descriptor matrix for this point is then computed. Finally, a DCT transformation is performed on the Daisy descriptor matrix, and the low‐frequency portion is processed using the perceptual hashing algorithm to generate a 32‐bit binary feature vector for the medical image. This scheme utilises cryptographic knowledge and zero‐watermarking technique to embed watermarks without modifying medical images and can extract the watermark from test images without the original image, which meets the basic requirements of medical image watermarking. The embedding and extraction of watermarks are accomplished in a mere 0.160 and 0.411s, respectively, with minimal computational overhead. Simulation results demonstrate the robustness of the algorithm against both conventional attacks and geometric attacks, with a notable performance in resisting rotation attacks.

Robust watermarking of medical images using SVM and hybrid DWT-SVD

Robust watermarking of medical images using SVM and hybrid DWT-SVD

Analysis of DWT-DCT watermarking algorithm on digital medical imaging.

Over the past decade, the diagnostic information of the patients are digitally recorded and transferred. During the transmission of patients data, the security and authenticity of the information has to be ensured. Medical image watermarking technology has recently advanced because it can be used to conceal patient information while ensuring the authenticity. We propose a multiple watermarking method for securing clinical medical images. In this watermarking method, the quality feature property and private label property information are embedded as watermarks in the original image. Initially, medical images are divided into the region of interest (ROI) and non-interest region (NIR). Second, a two-level discrete wavelet transform (DWT) is applied to the ROI and the coefficients LL1 (LL2, LH2, HL2, HH2), LH1, HL1, and HH1 are generated. Watermarks are embedded using the DWT low-frequency sub-band (LL2) by quantizing the low-frequency coefficients. Next, the NIR is separated into non-overlapping blocks, and a discrete cosine transform (DCT) is applied for each block. The DCT coefficients of each block are sorted using the zigzag transform. For embedding, eight intermediate frequency coefficients are used. Finally, the feature information is embedded in the ROI, and the tag information is embedded in the NIR. The performance of the DWT-DCT watermarking method is calculated using the metrics of peak signal-to-noise ratio (PSNR), structural similarity index measure, and mean square error. The proposed method obtained the better PSNR value of 45.76dB. The proposed model works well for clinical medical images when compared with the existing techniques.

A new highly fractal 1D- chaotic map-based novel encryption on HVS-based watermarked Fundus images for 2-level security in teleophthalmology applications

In recent years the combination of medical image watermarking and encryption is becoming popular due to a better trade-off between authenticity and security, since, each of these methods alone is insufficient to offer complete security. In the proposed work, the watermark is embedded into the Fundus image using the Singular Value Decomposition (SVD) of the La*b* space image of the Fundus image. The scaling factor is selected based on the HVS property of the human eye. The proposed embedding algorithm is semi-blind because the system requires the singular values (s) of the La*b* image at the receiver side in order to extract the watermark. To improve security, the authenticated image then undergoes 2-levels of confusion process followed by diffusion for encryption. For diffusion, the key is generated from the newly developed Modified Sine-Logistic Chaotic System (MSLCS), which is constructed by the combination of logistic map and sine map. The performance evaluation of the new chaotic system has been done in terms of the Bifurcation diagram, Lyapunov exponent, Phase portrait and Entropy. Also, Different tests such as Blood vessel segmentation and Retinal object identification were carried out on the original image, watermarked image, decrypted image and recovered image. The results show that the embedding of watermarks followed by encryption, on Fundus images does not affect the diagnosis and identification of retinal objects. Correct decryption, secured recovery of Fundus image and guaranteed extraction of the watermark at the receiver side make the proposed system secure for Fundus images in tele-ophthalmology applications.

Comparative Analysis of Watermarking Methods for DICOM Images: DWT and SVD vs. Traditional Techniques

Digital Imaging and Communications in Medicine (DICOM) images play a critical role in healthcare, necessitating robust methods for ensuring their integrity and authenticity. This research paper evaluates the effectiveness and efficiency of watermarking techniques applied to DICOM images, with a specific emphasis on comparing Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) approaches against traditional methods. The study examines three key criteria-robustness against attacks, imperceptibility to maintain diagnostic integrity, and computational performance-aiming to provide insights into the suitability of these techniques for protecting medical data. Results indicate significant differences in performance metrics, highlighting both strengths and limitations of each approach in the context of medical image watermarking.

Hybrid watermarking algorithm for medical images based on digital transformation and MobileNetV2

The current study presents a new methodology for zero watermarking that relies on the discrete wavelet transform (DWT) and an improved MobileNetV2 convolutional neural network, along with the discrete cosine transform (DCT). This model is intended to overcome the issue of algorithm robustness in encrypted medical image watermarking. The proposed technique targets medical images inside the area of encryption and offers a unique watermarking approach to overcome the issues above. The coefficients acquired from the fully linked network layer are converted using DWT and DCT to create the medical image's feature vector. Second, MobileNetV2 is initially fed the medical image; this network has been prepared by adjusting parameters such as the convolution kernels' size and the convolution modules' typical architecture. Finally, the encryption of watermarks is achieved through the utilization of the logistic map system and hash function, whereby an independent party securely stores the requisite keys. The integration of a zero-watermark involves the execution of logical operations on both the encrypted watermarks and the attributes of the source image. The results of the experiment indicate that the algorithm can effectively differentiate encrypted medical images and recover the initial data from encrypted watermarked material despite conventional and geometric attacks. Compared to alternative algorithms, their superior resilience and invisibility are noteworthy.

A New Approach for Roni Based Medical Image Watermarking Using Contourlet Transform

The majority of modern healthcare systems rely their diagnoses on data collected from the internet. The medical imaging and patient health records are transmitted over the network via the internet-enabled healthcare (E-health) applications. In practical medical applications, the primary problem is the security of medical images transmitted over the internet. For the purpose of embedding secret data on medical images without affecting important information, a trustworthy watermarking approach is required. We provide a unique watermarking approach in this research that embeds the watermark data in the medical image's Region of Non- Interest (RONI). The medical image is subjected to a contourlet transform, and RONI is determined using contourlet coefficients. In this work, we demonstrate the embedding and extraction process of medical image watermarking using Contourlet transform. Experiments are carried out on medical images such as MRI and CT images. The performance of the proposed scheme is analyzed using the metric PSNR. The proposed scheme provides an average PSNR value of 47.1604% for MRI images and 57.5452% of PSNR for CT images.

Secure medical image watermarking based on reversible data hiding with Arnold's cat map

The process of restoring medical images to their original form after the extraction process in application watermarking is crucial for ensuring their authenticity. Inaccurate diagnoses can occur due to distortions in medical images from conventional data embedding applications. To address this issue, reversible data hiding (RDH) method has been proposed by several researchers in recent years to embed data in medical images. After the extraction process, images can be restored to their original form with a reversible data-hiding method. In the past few years, several RDH methods have been rapidly developed, which are based on the concept of difference expansion (DE). However, it is crucial to pay attention to the security of the medical image watermarking method, the embedded data with RDH method can be easily modified, accessed, and altered by unauthorized individuals if they know the employed method. This research suggests a new approach to secure the RDH method through the use of Chaotic Map-based Arnold's Cat Map algorithms on the medical images. Data embedding was performed on random medical images using a DE method. Four gray-scale medical image modalities were used to assess the proposed method's efficacy. In our approach, we can incorporate capacity up to 0.62 bpp while maintaining a visual quality up to 41.02 dB according to PSNR and 0.9900 according to SSIM. The results indicated that it can enhance the security of the RDH method while retaining the ability to embed data and preserving the visual appearance of the medical images.

An Optimized Medical Image Watermarking Approach for E-Health Applications

Background: In recent years, information and communication technologies have been widely used in the healthcare sector. This development enables E-Health applications to transmit medical data, as well as their sharing and remote access by healthcare professionals. However, due to their sensitivity, medical data in general, and medical images in particular, are vulnerable to a variety of illegitimate attacks. Therefore, suitable security and effective protection are necessary during transmission. Method: In consideration of these challenges, we put forth a security system relying on digital watermarking with the aim of ensuring the integrity and authenticity of medical images. The proposed approach is based on Integer Wavelet Transform as an embedding algorithm; furthermore, Particles Swarm Optimization was employed to select the optimal scaling factor, which allows the system to be compatible with different medical imaging modalities. Results: The experimental results demonstrate that the method provides a high imperceptibility and robustness for both secret watermark and watermarked images. In addition, the proposed scheme performs better for medical images compared with similar watermarking algorithms. Conclusion: As it is suitable for a lossless-data application, IWT is the best choice for medical images integrity. Furthermore, using the PSO algorithm enables the algorithm to be compatible with different medical imaging modalities.

Optimization technique for optimal location selection based on medical image watermarking on healthcare system

The optimization algorithms mimic the process of natural evolution. In watermarking, appropriate positions to insert the watermark is identified by the image that covers. These positions represent the populations of genetic algorithms. The major drawback in genetic algorithm are that it may get stuck-up at a local optimum while moving towards the best global solution and hence the result is poor when compared to other local optimization techniques. The proposed work based on Bandelet based biogeography firefly hybrid algorithms. The Number of pixels, Intensity of the pixel and contrast are considered for watermarking. The redundancy is reduced by Bandelet and used to determine the best location to embed the information into an image both locally and globally. Results of these techniques are compared based on coefficient correlation, index structural similarity, and noise ratio from peak signal.

Robust medical image watermarking in frequency domain

Protecting patient information in medical image watermarking poses a significant challenge, especially when traditional methods like the Arnold transform prove inadequate in ensuring security. This paper introduces a novel approach within the Discrete Wavelet Transform (DWT) domain to address this issue effectively. By employing the Advanced Encryption Standard (AES), the security and robustness of the system are greatly enhanced through the encryption of both the medical image and patient data. The encrypted medical image undergoes a 2-level DWT process, allowing the concealment of encrypted patient information while maintaining its invisibility. This proposed scheme surpasses others in experimental evaluations, as evidenced by metrics such as PSNR and NC, solidifying its position as a more secure choice for medical image watermarking. The results validate the scheme’s robustness and imperceptibility.