ABSTRACT In medical domains, with the rapid development of internet activities, medical professionals have widely used medical information. Secure management and transmission of medical images, which is crucial to facilitate association, while protecting sensitive patient details. Nowadays, the growth of digitalization is providing convenience and competence that have expressively sensitive risks related to cybersecurity, which makes the security of medical information more significant than ever. However, healthcare application content can be easily stored, tampered with, and shared by unauthorized users. Consider these limitations; the concept of watermarking is to conceal secret systems while maintaining significant features like visual quality and robustness. But, the traditional watermarking process has several limitations, such as computational cost, security concerns, and low embedding ability. Here, this research framework introduces a deep learning (DL) based secure and robust image watermarking approach that is combined with chaotic encryption methods. Initially, this model utilizes the adaptive osprey optimization algorithm (AOOA) for selecting optimal locations in cover images. Following this, a chaotic‐based encryption process is used to encrypt secret images, which increases security performance. Proposed model presents depthwise separable convolutional assisted generative adversarial network used for embedding secret images into cover images. This model includes a depthwise separable convolution layer to decrease computational complexity and a GAN model for increasing the ability of the watermarking process that enhances visual quality. Experimental analysis, proposed model achieves a PSNR value of 68.9754 dB, a SSIM value of 0.9925, and an accuracy value of 99.45%, which are compared with existing models and state‐of‐the‐art models to demonstrate performance evaluation and robustness, security as well as invisibility performances in the medical image watermarking process.
Sinha et al. (Sat,) studied this question.