Medical imaging is pivotal in modern healthcare, enabling clinicians to visualize and diagnose various ailments. However, the security and integrity of these images are increasingly threatened by unauthorized access, tampering, or data breaches. Incorporating robust watermarking techniques is imperative to mitigate these risks, ensuring medical imagery’s authenticity and confidentiality [1,2,3]. Watermarking has emerged as a critical mechanism in image security, embedding imperceptible information within images to authenticate and protect against unauthorized alterations. While various watermarking techniques exist, applying such methods to medical images necessitates unique considerations due to the sensitivity and criticality of the data [4,5,6].

The current landscape of medical image watermarking research is multifaceted, showcasing an array of approaches and methodologies [7,8]. Several published works have highlighted the importance of robustness, imperceptibility, and computational efficiency in watermarking algorithms [9,10]. However, these methods often grapple with trade-offs between robustness and imperceptibility, challenging maintaining image quality while ensuring robust security measures. Controversies and diverging hypotheses persist, particularly concerning developing watermarking techniques explicitly tailored for medical images. Some studies advocate for highly complex algorithms, prioritizing an intricate embedding process for enhanced security, while others underscore the significance of simplicity to ensure computational efficiency without compromising robustness [11].

Telemedicine applications face a substantial vulnerability to cyber threats that severely impact the confidentiality, integrity, and authentication of sensitive medical data. The surge in internet usage, coupled with the widespread adoption of smartphones, mobile healthcare devices, and wearable health technology, has significantly propelled the growth of telemedicine [12,13]. This growth has led to the vast exchange and storage of electronic health records among physicians, patients, and healthcare professionals, aiming to enhance healthcare services. Among these records, multimedia, especially images obtained from various medical imaging technologies like X-rays, ultrasound, digital mammography, CT scans, PET scans, and MRI scans, play a critical role [14,15].

However, these medical images, being highly sensitive, are managed within a resource-constrained environment characterized by limited bandwidth, processing power, and memory [16,17]. The extensive use of these images necessitates robust security measures that accommodate stringent privacy requirements while operating under these resource limitations [18,19].

The significance of safeguarding these images lies in the fact that they are crucial for accurate diagnoses and treatment decisions in healthcare [20]. Ensuring these images’ integrity, authenticity, and confidentiality is fundamental, considering the potential repercussions of unauthorized access, tampering, or data breaches. Incorporating robust security algorithms tailored explicitly for resource-constrained environments is imperative for mitigating these risks. These algorithms must strike a balance between providing robust protection for sensitive medical images and operating within the limitations of the systems used in telemedicine. Finding a way to ensure data security without compromising processing efficiency becomes essential for maintaining the trustworthiness and privacy of these medical records within the telemedicine ecosystem [21,22].

Hasan et al. [23] introduced a lightweight encryption technology to safeguard the privacy of patients’ medical images. The paper explores diverse security measures, components, and methods for encrypting medical images. It delves into analyzing various existing encryption techniques, assessing their encryption quality, memory requirements, and execution time. The investigation reveals that current methods involving key-based unsystematic sequence numbers result in extensive computational time. In contrast, the study’s results demonstrate that the proposed algorithm significantly reduces computational load. Thus, the meticulously devised algorithm aims to optimize security while ensuring minimal computational overhead to protect medical images. This encryption methodology operates through three stages, employing a 256-bit key value for logical operations on the images.

The work conducted by Araghi et al. [24] presented a novel watermarking scheme based on a Discrete Wavelet Transform (DWT) and 2-D Singular Value Decomposition (SVD). The innovation in this scheme primarily revolves around employing a two-level SVD, thereby enhancing its efficiency to be independent of the host image’s size. Comparative analysis was carried out between the proposed scheme and the authors’ prior DWT and 2-D SVD scheme, utilizing the same host and watermark images, attack parameters, and conditions. The experimental findings demonstrated that the new scheme achieved greater imperceptibility and robustness than the authors’ previous scheme and exhibited superior performance when contrasted with conventional DWT + SVD schemes. Moreover, a two-level authentication system was incorporated to ensure security, effectively identifying false positive and negative outcomes. Additionally, the proposed scheme addressed the limitations of the authors’ prior DWT and 2-D SVD schemes through image blocking and introduced a formula to optimize efficiency for increased capacity.

The method proposed by Khare et al. in [25] introduces an innovative approach for medical image watermarking (MIW) that integrates key features of Hough Transform (HT), Redundant Discrete Wavelet Transform (RDWT), and Singular Value Decomposition (SVD) transformations. The watermark embedding within the reflectance component ensures improved robustness and perceptual invisibility. To fortify resistance against attacks, a combination of RDWT and SVD is utilized. The technique incorporates a dual-layer security mechanism for the watermark image by employing chaotic mapping to safeguard critical diagnostic medical data against manipulation or unethical activities. Furthermore, the performance of the proposed technique is evaluated across several wavelet families. The experimental results notably demonstrate the superior robustness and perceptibility achieved by the proposed scheme, as various performance metrics exhibit enhanced values.

The novel strategy developed by Zermi et al. in [26] involves meticulously integrating hospital signature information and patient data within medical images. The primary aim of this endeavor is to seamlessly embed the watermark with minimal distortion, ensuring the preservation of essential medical information within the image. Employing a flexible approach, the initial step involves applying Discrete Wavelet Transform (DWT) decomposition to the image, enabling a highly adaptable adjustment during insertion. Subsequently, an SVD is employed on the three sub-bands, LL (Low-Low), LH (Low-High), and HL (High-Low), allowing for the preservation of maximum image energy using the essential minimum of singular values.

The research conducted by Alshanbari Hanan S. highlighted the effectiveness of multiple watermark insertions in addressing various security aspects of images, including ownership verification, tamper detection, and Region of Interest (ROI) recovery. The insertion of robust and fragile watermarks in succession was deemed essential to ensure the flawless operation of the proposed algorithm. Utilizing the principal components of the watermark for embedding purposes provided the proposed scheme’s resilience against security errors. Additionally, the application of DWT played a pivotal role in enhancing the watermarking scheme by improving imperceptibility and robustness [27].

In this investigation, Aparna et al. in [28] detailed a medical image watermarking technique employing a variety of algorithms. The utilization of biometric fingerprint technology notably enhances the security of the watermarking system. The study delved into analyzing various methods, including the RG algorithm, SHA-256, minutiae point extraction, elliptical curve cryptography, arithmetic encoding, and the embedding and extraction processes. The assessment focused on medical image integrity, authentication, and confidentiality.

In [29], Amine, Khaldi, et al. presented a robust and imperceptible watermarking method to secure medical images utilized in telemedicine. This technique is tailored to ensure traceability and integrity, fortifying the security of crucial medical data within telemedicine. Their paper introduces a blind watermarking methodology designed to secure electronic patient records effectively. The process involves a meticulous amalgamation of successive values’ parity. This innovative method is implemented across three insertion domains: spatial, frequency, and multiresolution. The watermark is intricately integrated within the image’s colorimetric values for spatial insertion. In the frequency domain, the least significant bit of the Discrete Cosine Transform (DCT) coefficients is replaced with the watermark bits. The integration process employs the LL sub-band coefficients obtained post-Discrete Wavelet Transform (DWT) computation in the multiresolution domain. Upon comparison with recent works in these domains, their approach exhibits noteworthy imperceptibility, particularly in the frequency and spatial domains.

In [30], a study by an undisclosed author proposes applying crypto-watermarking for securing medical images in E-healthcare settings. The work presents an effective crypto-watermarking system integrating cryptographic algorithms and embedding processes. This method is adaptable to various modalities of medical images and is suitable for different image sizes, formats, and bit depths. Notably, including face images enhances the security of the crypto-watermarking system. The analysis includes examining the region-growing algorithm, SHA-256, AES, arithmetic encoding, and the embedding and extraction processes.

Chao et al. proposed an innovative data concealment technique to share digital medical information between different healthcare facilities securely. Their method involved merging various types of medical data into an encoded image, accessible only to authorized individuals during extraction. However, this approach has limitations, notably in its incapacity to detect tampering or perform self-recovery in the event of data corruption or manipulation [6].

On a different front, Guo and Zhuang introduced a watermarking system designed to authenticate and ensure the integrity of medical images. While this system allows for complete image recovery without loss, it operates on a non-blind framework, requiring authentication of the original watermark data, such as electronic patient records. This reliance on specific original data presents a weakness, as it could pose challenges if the original information is unavailable or compromised, potentially hindering the verification process. Additionally, the non-blind nature of this method could lead to security vulnerabilities if unauthorized individuals gain access to the original data, compromising the authentication process [31].

Moreover, both techniques might need help with scalability and compatibility in larger healthcare systems, where diverse data formats and multiple access points could pose challenges in implementing and maintaining these security measures across different platforms and systems within various healthcare facilities. Furthermore, the potential complexity of integrating these methods into existing hospital systems might create barriers to their widespread adoption. It could necessitate extensive training or system updates, making their implementation less straightforward.

In health services information systems, the relevance of medical data in the diagnostic process is paramount. Most healthcare services rely on external systems to store patient information, making the security of these systems of utmost importance. With the advancements in communication and multimedia technologies, digital elements can be manipulated, copied, and replicated without leaving discernible traces, emphasizing the critical need for robust security measures. The effective transfer of medical information over public networks is significantly evolving, especially in telemedicine, telediagnosis, telesurgery, distance learning, and applications related to private database consultations. Transfer conditions mirror those encountered in electronic commerce, subjecting specific medical images to potential vulnerabilities such as communication errors and lossy compression. Preserving the integrity of medical information within the images is particularly crucial. To address these challenges, a paper (10.3390/app132413291) introduces a novel blind Fast Discrete Curvelet Transform (FDCuT), Discrete Cosine Transform (DCT), and RSA-based medical image watermarking technique. The proposed algorithm demonstrates superior performance, specifically in terms of Peak Signal-to-Noise Ratio and maximum embedding capacity.