Speckle-based optical cryptosystem and its application for human face recognition via deep learning

TL;DR

This paper introduces a speckle-based optical cryptosystem that encrypts face images using physical secret keys generated by light scattering, enabling high-speed decryption and face recognition with high accuracy, enhancing security and privacy.

Contribution

A novel, efficient optical cryptosystem utilizing speckle patterns for secure face image encryption and deep learning-based decryption, offering longer keys and faster processing.

Findings

Achieved up to 98% face recognition accuracy.

Generated gigabit-length secret keys via optical speckles.

Demonstrated high-speed encryption and decryption.

Abstract

Face recognition has recently become ubiquitous in many scenes for authentication or security purposes. Meanwhile, there are increasing concerns about the privacy of face images, which are sensitive biometric data that should be carefully protected. Software-based cryptosystems are widely adopted nowadays to encrypt face images, but the security level is limited by insufficient digital secret key length or computing power. Hardware-based optical cryptosystems can generate enormously longer secret keys and enable encryption at light speed, but most reported optical methods, such as double random phase encryption, are less compatible with other systems due to system complexity. In this study, a plain yet high-efficient speckle-based optical cryptosystem is proposed and implemented. A scattering ground glass is exploited to generate physical secret keys of gigabit length and encrypt face images via seemingly random optical speckles at light speed. Face images can then be decrypted from the random speckles by a well-trained decryption neural network, such that face recognition can be realized with up to 98% accuracy. The proposed cryptosystem has wide applicability, and it may open a new avenue for high-security complex information encryption and decryption by utilizing optical speckles.