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MAN-C: A masked autoencoder neural cryptography based encryption scheme for CT scan images.

Kishore Kumar¹, Sarvesh Tanwar¹, Shishir Kumar²

¹Amity Institute of Information Technology, Amity University, Noida, Uttar Pradesh, India.

|May 8, 2024

Summary

We introduce MAN-C, a novel encryption scheme using masked autoencoders and neural cryptography for secure medical image sharing. This method enhances data confidentiality and image recovery during transmission.

Keywords:

Auto encoder Hebbian learning Image encryption MAN—C: a Masked Autoencoder Neural Cryptography based Encryption Scheme for CT Scan Images Masked transformer Neural cryptography Secret sharing Tree parity machine

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Area of Science:

Medical Imaging
Cryptography
Computer Vision

Background:

Secure sharing of medical images is crucial for patient data confidentiality.
Existing methods may face challenges in maintaining image integrity during transmission.

Purpose of the Study:

To propose MAN-C, a Masked Autoencoder Neural Cryptography based encryption scheme for secure medical image sharing.
To leverage masked autoencoders and neural cryptography for enhanced security and image reconstruction.

Main Methods:

Utilizing masked autoencoders, originally for self-supervised learning in computer vision, as an encryption-decryption mechanism.
Integrating autoencoders with neural cryptography, including a tree parity machine and Shamir Scheme, for secret image sharing.
Employing CT scans from The Cancer Imaging Archive (TCIA) for evaluation.

Main Results:

The MAN-C scheme demonstrates improved Root Mean Square Error (RMSE) values compared to existing techniques.
Comparable correlation values between input and output images were achieved.
The method effectively recovers image loss caused by noise during secret sharing.

Conclusions:

MAN-C offers a secure and efficient method for sharing medical images.
The combination of masked autoencoders and neural cryptography provides a robust encryption solution.
This approach enhances confidentiality while maintaining image quality and integrity.