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

Abstract

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.

Explore the potential of deep learning and hyperchaotic map in the meaningful visual image encryption scheme

“…The network structure is shown in Figure 5. We introduce the method of [45] to replicate the 1‐channel grey image into a 3‐channel. In this way, the three‐channel grey image can be considered as a special colour image, so that the trained network can be applied to both grey images and RGB colour images without separate training.…”

A deep learning signed medical image based on cryptographic techniques

“…As shown in Table 6 which displays the comparative results assessing our proposed system with older systems due to their NPCR and UACI values, from the following analysis, it has been proved that the proposed scheme is robust and can withstand the differential assaults. [35] 0.9961 0.3357 Ding et al [23] 0.9959 0.2319 Bao and Xue [39] 0.9964 0.3349…”

“…In any crptographic system, expressing the degree of uncertainty is measured the information entropy [35]. It can also be used in expressing the uncertainties in image information by measuring the grey values distribution in an image.…”

“…Thus, the results confirm that our new suggested scheme can be able to effectively counteroffensive the information entropy. [35] 7.9944 Ding et al [23] 7.9986 Bao and Xue [39] 7.9972…”

Survey on deep learning applications in digital image security

“…Image compression can increase the efficiency of image encryption by reducing the size of data. Chen et al 106 utilized a deep learning model trained on a color image dataset 107 to compress and reconstruct the plaintext image and compound the chaotic system to encryption. Hu et al 108 employed stacked autoencoder for compression and chaotic logistic map to encrypt the compressed vector.…”