Speckle-Based Optical Cryptosystem and its Application for Human Face Recognition via Deep Learning

Abstract

Face recognition has become ubiquitous for authentication or security purposes. Meanwhile, there are increasing concerns about the privacy of face images, which are sensitive biometric data and should be protected. Software-based cryptosystems are widely adopted 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 highly efficient speckle-based optical cryptosystem is proposed and implemented. A scattering ground glass is exploited to generate physical secret keys of 17.2 gigabit length and encrypt face images via seemingly random optical speckles at light speed. Face images can then be decrypted from random speckles by a well-trained decryption neural network, such that face recognition can be realized with up to 98% accuracy. Furthermore, attack analyses are carried out to show the cryptosystem's security. Due to its high security, fast speed, and low cost, the speckle-based optical cryptosystem is suitable for practical applications and can inspire other high-security cryptosystems.