# Invisible Shield: Unveiling an Efficient Watermarking Solution for Medical Imaging Security
## 1 Introduction  
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-offsbetween 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]. This paper aims to contribute to this evolving landscape by presenting an efficient watermarking algorithm customized for medical images. Our primary objective is to develop a technique that optimally balances robustness, imperceptibility, and computational efficiency. This algorithm intends to offer a pioneering solution that fortifies the security of medical images and maintains their diagnostic integrity.