1. Introduction
The development of electronic healthcare systems in smart cities and the support of the medical community have gained popularity over the past decade. In electronic healthcare systems, medical information such as medical reports and images (i.e., USG, CT scan, MRI, or X-ray) is transmitted via interactive audiovisual medium for the purpose of consultation and occasionally for distant medical operations or tests. The medical images used in an electronic healthcare system are extremely vulnerable to any data breach since they contain sensitive patient information required for the treatment of various ailments. Any alteration to these medical records could result in a misdiagnosis that could lead to incorrect treatment or even death. Medical images include image metadata and information on the patient’s medical history [
1]. According to the medical imaging protocol, the image header contains a generic metadata format. A crucial aspect of medical diagnosis for patient monitoring is contingent upon the capacity of radiologists to conduct a reliable diagnosis based on the obtained images. The determination of the diagnosis is heavily dependent on a thorough visual examination of the morphology of the lesions. The implementation of user-friendly interfaces is of the utmost importance in facilitating accurate visual examination by radiologists and enabling the efficient identification of lesions [
2,
3]. The patient information, as well as the medical imaging and acquisition features, are all described in the metadata. This information is susceptible to being lost or changed; the images should be viewed with suspicion during transmission in electronic healthcare systems. A high level of privacy and security is required whenever sensitive digital medical images are stored, shared, or transferred over a network. Throughout the transmission process, digital medical images are vulnerable to being accidentally distorted by signal processing techniques such as compression, noise, segmentation, etc. [
4,
5,
6,
7]. In the case of medical images, legitimacy is another important issue [
8]. Therefore, it is important to put a lot of emphasis on legitimacy and integrity when sharing medical images [
9,
10,
11]. Steganography [
12], data encryption [
13], and digital watermarking [
14] are some examples of useful techniques to secure digital content. However, digital image watermarking provides efficient ways to protect and secure digital images from malicious or unintentional distortion as well as safeguard the confidentiality of patients [
7,
15,
16].
Watermarking has been an important research topic for decades. It is one of the most widely used methods of protecting digital content. It is advisable to employ an invisible watermark that is indecipherable to human senses. Effective image watermarking systems must balance imperceptibility and robustness, register ownership information, and demonstrate dependability [
17,
18]. Robustness in watermarking means recognizing a watermark despite well-known attacks. A secure watermarking system should prevent detection, removal, and alteration [
19]. Moreover, another important aspect of watermarking schemes is that they are imperceptible to the human visual system. In certain watermarking applications, however, slight distortions are acceptable in order to obtain higher robustness at a lower price [
14,
17]. In addition, security and payload are also regarded as important aspects when designing a watermarking scheme [
20]. In the literature, there are several methods of image watermarking that were developed to meet the aforementioned objectives. A robust watermarking scheme should sustain itself against a wide range of attacks such as compression, filtering, noise addition, etc. Since Hessenberg decomposition offers remarkable features in image watermarking for the development of robust and secured watermarking schemes, Many researchers have used Hessenberg in their robust and secure watermarking schemes. Moreover, the transform domain is often used to improve the imperceptibility of a watermarking scheme. There are several reports available in the literature, such as [
17,
18,
20,
21,
22,
23,
24,
25,
26,
27,
28,
29,
30,
31,
32].
Abdallah et al. [
23] used non-negative matrix factorization and three-dimensional mesh spectra to develop a secure and robust watermarking scheme. The fundamental concept entails iteratively applying encryption to a watermark vector; then, the data are subsequently integrated into the spectral entries of a compact 3D mesh. This method requires the removal of the original object and is non-blind, but it is resistant to a variety of attacks. Su and Chen [
22] presented blind, robust watermarking for spatial colour images. This approach separates the cover image into red, green, and blue (RGB) channels and embeds it in the blue channel. The process of embedding a binary watermark involves partitioning it into four sub-watermarks, and the blue channel pixel values are manipulated to embed the elements into the distinct areas of the cover image. The aforementioned task is achieved by employing the direct current coefficient and the primary characteristics of generation. The reconstruction or recovery of the watermark image is accomplished through the utilisation of DC coefficients, quantization based on a key, and various statistical principles and procedures. This approach was deemed secure due to its utilisation of encryption and confidential keys. Selesnick [
24] presented a reliable discrete cosine transform (DCT)-based watermark for blind grayscale images. The method under consideration employs mixed modulations and a partially sign-altered mean in the utilisation of the discrete cosine transform, which facilitates the attainment of data balance and enables the embedding of multiple coefficients, thereby enhancing attack strength while maintaining a low bit error rate. The proposed scheme demonstrates enhanced resilience and visual excellence with an average of 40 dB. Furthermore, Wang et al. [
33] presented a hybrid and resilient approach for embedding watermarks in colour images through the utilisation of Discrete Wavelet Transform (DWT) and LU decomposition. The proposed methodology involves an initial DWT-based transformation of the cover image. Subsequent to the initial stage, the Low-High (LH) and High-Low (HL) sub-bands are subjected to a process of selection and separation, resulting in the formation of discrete and mutually exclusive blocks. The blocks are subjected to LU decomposition, following which the encrypted watermark image is incorporated into the upper triangular matrices of the blocks. The plan under consideration exhibits a significant degree of resilience. Moreover, Liu et al. [
25] suggested using the Affine transformation and Schur decomposition to create a reliable blind colour image watermarking system. The upper triangular matrix of the Schur decomposition is used to quantify the diagonal eigenvalues after the watermark image has been encrypted using the Affine transformation. The suggested scheme has low computational requirements and good resistance to numerous attacks. Hsu and Hu [
26] presented a reliable DCT-based watermark for blind grayscale images. The proposed method utilises advanced techniques to enhance the security of the data and increase its strength against potential attacks. The proposed scheme offers excellent robustness and imperceptibility with a low bit error rate and an average of 40 dB. Another study investigated two distinct applications utilising deep learning-based generative adversarial networks (GANs) and transfer learning for magnetic resonance imaging (MRI) reconstruction procedures for brain and knee imaging. The approach facilitates the implementation of forthcoming MRI reconstruction models, obviating the need for extensive imaging datasets [
34,
35,
36].
Additionally, Kalra et al. [
37] introduced a robust watermarking technique for colour images that utilised DWT and DCT transformations. The watermark image is well protected with multiple security-enhancing techniques before embedding. The embedding process involves the selection of the middle-frequency band, followed by the application of
blocking and DCT. This is applied to the cover image’s two-level DWT decomposition’s middle-frequency band. The cover image is now ready to undergo the two-level DWT process after being shrunk. By determining the location of the watermark pixel for non-overlapping
blocks using column and row numbers, we can successfully embed the watermark. Furthermore, the watermark is securely embedded within the chosen blocks using a sophisticated low-frequency technique. Balasamy and Ramakrishnan [
27] proposed a novel watermarking scheme based on the wavelet transform and particle swarm optimization (PSO). In this scheme, the host image is transformed using wavelet transform, and the watermark is protected using a tent map and hash function. The scheme achieves high PSNR. Saxena and Mishra [
21] used a variant of multi-objective-PSO to develop a watermarking scheme. The primary objective of this scheme is to select the leader with the shortest distance from the region that the particle has recently visited. Sisaudia and Vishwakarma [
28] proposed a kernel extreme learning machine and PSO-based watermarking scheme to secure watermarks. The PSO is used to optimize the scaling factor for the watermarking process. The scheme achieves high PSNR values for the watermarked images. Ali and Ahn [
38] presented DWT-singular value decomposition (SVD)-based self-adaptive differential evolution-based watermarking scheme in which multiple scaling factors are optimized to embed imperceptible watermarks. Kazemivash and Moghaddam [
39] has developed a robust and secure watermarking model by combining the regression tree and firefly algorithms to optimize scaling factors. Moeinaddini [
40] presented a watermarking scheme to balance imperceptibility and robustness by combing entropy and a distinct discrete firefly algorithm for optimization. Using the Bat optimization algorithm, Pourhadi and Mahdavi [
41] presented a robust and optimized digital image watermarking scheme based on the stationary wavelet transform to correct the geometric attack. Rajpal et al. [
42] proposed a watermarking scheme to optimize multiple scaling factors using online sequential extreme learning machine. Sharma and Mir [
43] used the meta-heuristic optimization approach to develop an optimized watermarking scheme. Idowu et al. proposed a framework-based statistical and morphological model that is capable of effectively executing concurrent denoising and enhancement procedures. The aim of this study was to devise a maximum a posteriori (MAP) estimator for the coefficient that is free from any noise. The utilisation of a statistical model eliminates the need for the estimation of the noise level and enables the model to automatically adapt to the observed image data. The methodology was devised with the aim of preserving the genuineness of the image’s delicate characteristics [
44].
To obtain a more robust and secure watermarking scheme, the majority of the aforementioned studies have shifted from traditional watermarking to either heuristic-search-based or nature-inspired watermarking techniques. Although many decomposition-based watermarking schemes exist, their efficacy is inferior. PSO has been used in a variety of research and application domains in recent years. PSO has gained widespread attention in recent years due to its ease of implementation and rapid convergence to acceptable solutions. Although the PSO method has been used to solve search and optimization problems, it is susceptible to becoming trapped in local optima. Therefore, the proposed technique combines the lifting wavelet transform (LWT) and the Hessenberg decomposition and approaches for nature-inspired optimization for watermark embedding. Extensive tests show that our proposed method outperforms previous approaches. The suggested optimal watermarking scheme provides better robustness. The major contributions of the proposed scheme are as follows:
The utilisation of the two-level transform LWT was employed to achieve the robustness of watermarked ultrasound medical images.
The use of the Arnold transform enhances the security of watermarked images.
The Hessenberg decomposition is employed to attain a high level of imperceptibility in watermarked images.
The PSO technique was employed to determine the optimal value of the multi-valued scaling factor in the proposed watermarking methodology.
The rest of the paper is organized as follows:
Section 2 provides an overview of the techniques we use and provides context for the conversation that follows.
Section 3 explains the suggested research approach for watermark embedding and extraction.
Section 4 discusses the results.
Section 5 concludes our work.