一种基于深度学习的双随机相位-振幅加密图像重建方法

Reconstruction of Double Random Phase Amplitude Image Encryption Based on Deep Learning

针对基于双随机相位振幅编码(DRPAE)技术的加密系统,提出了一种基于ResNet网络的已知明文攻击(KPA)算法。提出的攻击主要通过在残差网络中,利用一系列明文-密文振幅图像对数据集来完成训练集的制作。将明文-密文输入神经网络中进行训练可以让神经网络拟合从密文到明文的加密过程,独立于训练集之外的密文通过应用训练好的网络实现对原始图像的有效恢复处理,从而达到了攻击加密系统,实现对图像解密的效果。从理论上分析了该攻击方法的有效性和可行性,并通过计算机仿真进行了验证。

A known plaintext attack(KPA)algorithm based on ResNet is proposed for encryption system based on double random phase amplitude encoding(DRPAE). Our attack mainly uses a series of plaintext-ciphertext amplitude image pairs to make training set in residual network. Inputting plaintext ciphertext into neural network for training can make neural network fit the encryption process from ciphertext to plaintext. Independent of the ciphertext outside the training set,the trained network can effectively recover the original image,so as to attack the encryption system and decrypt the image. In this paper,the validity and feasibility of the attack method are analyzed theoretically and verified by computer simulation.