Hidden in plain sight: AI-driven steganography and watermarking for secure transmission of ophthalmic data

Abstract

Purpose

To explore the application of artificial intelligence (AI) in enhancing steganographic and watermarking techniques for the secure transmission of ophthalmic data. This study aims to delineate the integration of these methods into healthcare frameworks to ensure data confidentiality, integrity, and compliance with regulatory standards.

Design and methods

A descriptive and analytical approach was employed to examine the potential of steganographic and watermarking techniques in ophthalmic data security. The study reviews historical and contemporary uses of these methods and introduces AI as a means to enhance their efficacy and application in medical data transmission. We applied an example use-case of an open-source steganography application that performs both data concealment and watermarking to demonstrate practical implementation.

Results

AI-enhanced steganography allows for the imperceptible embedding of sensitive patient data within digital ophthalmic images, which can significantly obscure the presence of transmitted data from unauthorized parties. Similarly, AI-driven watermarking can embed digital signatures to authenticate image origins and signal alterations, aiding in forensic integrity and compliance verification.

Conclusion

Integrating AI with steganography and watermarking offers promising enhancements to the security and efficiency of ophthalmic data transmission. While these AI-driven techniques contribute to a more robust data-handling framework, their successful deployment requires interdisciplinary collaboration and continuous refinement to address emerging technical and ethical challenges effectively.