论文标题
基于斑点的光密码系统及其通过深度学习的应用程序识别的应用
Speckle-based optical cryptosystem and its application for human face recognition via deep learning
论文作者
论文摘要
在许多场景中,出于身份验证或安全目的,面部识别已变得无处不在。同时,人们对面部图像的隐私越来越担心,这些图像是敏感的生物特征数据,应仔细保护。如今,基于软件的密码系统已被广泛采用以加密面部图像,但是安全级别受到数字秘密密钥长度或计算能力不足的限制。基于硬件的光密码系统可以生成较长的秘密键,并以光速启用加密,但是由于系统复杂性,大多数报道的光学方法(例如双重随机相加密)与其他系统兼容。在这项研究中,提出并实施了一个普通但高效的基于斑点的光密码系统。利用散射地面玻璃来生成千兆长的物理秘密钥匙,并通过轻度速度通过看似随机的光学斑点来加密面部图像。然后,可以通过训练有素的解密神经网络将面部图像从随机斑点中解密,从而可以以高达98%的精度来实现面部识别。拟议的密码系统具有广泛的适用性,它可能通过利用光学斑点为高安全性复杂信息加密和解密开辟了新的途径。
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.