基于自动编码器的光学图像加密方法

Optical Image Encryption Method Based on Autoencoder

为了保护图像隐私、解决光学图像加密对光学仪器精密度依赖度高的问题,提出了基于自动编码器的光学图像加密方法。该方法使用深度神经网络模拟双随机相位编码,通过在输入中加入目标随机图像模仿第一层随机相位模板,利用编码网络的卷积核模仿第二层随机相位模板。卫星图像实验结果表明,本文方法能有效地加密光学图像,直方图上密文图像像素点分布较为均匀。和基于CycleGAN的加密方法相比较,本文方法更简单,计算资源消耗更少,加密性能不受光学仪器性能的影响,加密后图像的峰值信噪比(PSNR)值下降了6.5745,水平、垂直和对角方向上相邻像素的相关系数绝对值分别低了0.110375,0.118625,0.01335,解密后图像的PSNR值上升了1.4075,结构相似性值高了0.0428。

In order to protect the image privacy and solve the problem that optical image encryption relies on high precision of optical instrument,an optical image encryption method based on an autoencoder is proposed.In this method,a deep neural network is used to simulate double random phase encoding.The target random image added into the input is used for simulating the first random phase template,and the convolution kernel of the encoding network is used for simulating the second layer random phase template.The experimental results of satellite images show that this method can effectively encrypt optical images,and the pixel distribution of ciphertext images on histograms is relatively uniform.Compared with the encryption method based on CycleGAN,this method is simpler and consumes less computing resources.The peak signal-to-noise ratio(PSNR)value of the encrypted image decreases by 6.5745,and the absolute values of the correlation coefficients of adjacent pixels in the horizontal,vertical,and diagonal directions decrease by0.110375,0.118625,and0.01335,respectively.The PSNR value of the decrypted image increases by 1.4075,and the structural similarity value increases by 0.0428.