基于深度学习压缩感知与复合混沌系统的通用图像加密算法

General image encryption algorithm based on deep learning compressed sensing and compound chaotic system

提出一种适用于灰度图像与RGB格式彩色图像的通用图像加密算法.利用双线性插值Bilinear与卷积神经网络对图像进行压缩,再使用二维云模型与Logistic组成的复合混沌系统对压缩图像加解密(滑动置乱与矢量分解),最后对解密图像进行重构.重构网络中,由卷积神经网络与双线性插值Bilinear主要负责重构轮廓信息,全连接层主要负责重构颜色信息.实验结果表明,该基于深度学习压缩感知与复合混沌系统的通用图像加密算法在数据处理质量和计算量上有着很大优势.由于复合混沌系统有着足够大的密钥空间且将明文哈希值与密钥关联,可实现一图一密的加密效果,能有效抵抗暴力攻击与选择明文攻击,与对比文献相比,相关系数更接近理想值且信息熵与明文敏感性指标也都在临界值范围内,其加密算法有着更高的安全性.

Many image compression and encryption algorithms based on traditional compressed sensing and chaotic systems are time-consuming,have low reconstruction quality,and are suitable only for grayscale images.In this paper,we propose a general image compression encryption algorithm based on a deep learning compressed sensing and compound chaotic system,which is suitable for grayscale images and RGB format color images.Color images can be directly compressed and encrypted,but grayscale images need copying from 1 channel to 3 channels.First,the original image is divided into multiple 3×33×33 non-overlapping image blocks and the bilinear interpolation Bilinear and convolutional neural network are used to compress the image,so that the compression network has no restriction on the sampling rate and can obtain high-quality compression of image.Then a composite chaotic system composed of a two-dimensional cloud model and Logistic is used to encrypt and decrypt the compressed image(sliding scrambling and vector decomposition),and finally the decrypted image is reconstructed.In the reconstruction network,the convolutional neural network and bilinear interpolation Bilinear are mainly responsible for reconstructing the contour structure information,and the fully connected layer is mainly responsible for reconstructing and combining the color information to reconstruct a high-quality image.For grayscale images,we also need to calculate the average value of the corresponding positions of the 3 channels of the reconstructed image,and change the 3 channels into 1 channel.The experimental results show that the general image encryption algorithm based on deep learning compressed sensing and compound chaos system has great advantages in data processing quality and computational complexity.Although in the network the color images are used for training,the quality of grayscale image reconstruction is still better than that of other algorithms.The image encryption algorithm has a large enough key space and associates the plaintext hash value with the key,which realizes the encryption effect of one image corresponding to one key,thus being able to effectively resist brute force attacks and selective plaintext attacks.Compared with it in the comparison literature,the correlation coefficient is close to an ideal value,and the information entropy and the clear text sensitivity index are also within a critical range,which enhances the confidentiality of the image.