Optical encryption-transmission via computational ghost imaging and fractional OAM multiplexing

Area of Science:

• Optical physics
• Information security
• Machine learning

Background:

• Computational ghost imaging (CGI) offers unique encryption capabilities.
• Fractional orbital angular momentum (FOAM) enables high-dimensional optical data transmission.
• Secure optical image transmission remains a critical challenge.

Purpose of the Study:

• To develop a novel, secure method for optical image encryption and transmission.
• To integrate CGI, FOAM, and deep learning for enhanced security and data capacity.
• To establish a robust authentication-decryption mechanism for optical signals.

Main Methods:

• Image encryption using CGI by reindexing bucket signals and converting reference signals to sparse sequences.
• Encoding encrypted signals onto FOAM modes with a rotational phase shift for secure transmission.
• Utilizing a pre-trained DenseNet for decryption by identifying FOAM topological charges from intensity patterns.
• Implementing an authentication mechanism prior to decryption to ensure signal legality.

Main Results:

• Successful encryption and secure transmission of optical images using the combined CGI-FOAM approach.
• Accurate decryption of images via DenseNet, demonstrating the effectiveness of the proposed method.
• Enhanced security through rotational phase shifts and an integrated authentication-decryption process.
• Demonstrated feasibility of interdisciplinary research between optical communication and information security.

Conclusions:

• The proposed method effectively combines CGI and FOAM for secure optical image encryption and transmission.
• Deep learning significantly aids in the decryption process, improving accuracy and efficiency.
• The integrated authentication mechanism provides an additional layer of security against unauthorized access.
• This research opens new avenues for secure OAM-based optical communication and CGI-based information security.