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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.
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Keywords:
- deep learning/
- compressed sensing/
- encryption/
- compound chaotic system
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采样率 算法 Lena Monarch Flinstones 平均PSNR 0.25 TVAL3 28.67 27.77 24.05 27.84 NLR-CS 29.39 25.91 22.43 28.05 D-AMP 28.00 26.39 25.02 28.17 ReconNet 26.54 24.31 22.45 25.54 DR2-Net 29.42 27.95 26.19 28.66 MSRNet 30.21 28.90 26.67 29.48 FCLBCNN 31.09 29.97 27.57 29.71 0.10 TVAL3 24.16 21.16 18.88 22.84 NLR-CS 15.30 14.59 12.18 14.19 D-AMP 22.51 19.00 16.94 21.14 ReconNet 23.83 21.10 18.92 22.68 DR2-Net 25.39 23.10 21.09 24.32 MSRNet 26.28 23.98 21.72 25.16 FCLBCNN 26.93 24.58 22.08 25.41 0.04 TVAL3 19.46 16.73 14.88 18.39 NLR-CS 11.61 11.62 8.96 10.58 D-AMP 16.52 14.57 12.93 15.49 ReconNet 21.28 18.19 16.30 19.99 DR2-Net 22.13 18.93 16.93 20.80 MSRNet 22.76 19.26 17.28 21.41 FCLBCNN 23.33 19.59 17.17 21.51 0.01 TVAL3 11.87 11.09 9.75 11.31 NLR-CS 5.95 6.38 4.45 5.30 D-AMP 5.73 6.20 4.33 5.19 ReconNet 17.87 15.39 13.96 17.27 DR2-Net 17.97 15.33 14.01 17.44 MSRNet 18.06 15.41 13.83 17.54 FCLBCNN 18.12 15.63 13.90 17.62 
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算法 MR= 0.08 MR= 0.10 MR= 0.18 MR= 0.25 MR= 0.53 PSNR SSIM PSNR SSIM PSNR SSIM PSNR SSIM PSNR SSIM ReconNet — — 23.28 0.6121 — — 25.48 0.7241 — — DR2-Net — — 24.26 0.6603 — — 27.56 0.7961 — — MSRNet — — 24.73 0.6837 — — 27.93 0.8121 — — FCLBCNN (gray) — — 24.83 0.7056 — — 28.19 0.8400 32.87 0.9392 FCLBCNN (color) 27.94 0.8252 — — 32.07 0.9279 — — — — DownLoad:
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测试图像 方向 明文(gray) 密文 本文(gray) 本文(color) 文献[20] 文献[21] Lena 水平 0.9396 0.0010 –0.0024 –0.0048 0.0011 竖直 0.9639 –0.0066 0.0012 –0.0112 0.0098 斜线 0.9189 –0.0039 0.0035 –0.0045 –0.0227 Peppers 水平 0.9769 –0.0004 –0.0023 –0.0056 0.0071 竖直 0.9772 0.0089 0.0063 –0.0162 –0.0065 斜线 0.9625 –0.0077 0.0004 –0.0113 –0.0165 平均值 水平 — 0.0003 –0.0024 –0.0052 0.0041 竖直 — 0.0012 0.0038 –0.0137 0.0017 斜线 — –0.0058 0.0020 –0.0079 –0.0196 DownLoad:
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测试图像 明文(gray) 密文 本文(gary) 本文(color) 文献[12] Lena 7.3035 7.9949 7.9944 7.9544 Peppers 7.4344 7.9956 7.9952 7.9633 DownLoad:
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测试图像 局部信息熵(gray/color) 临界值 $\begin{array}{l} u_{0.05}^{* - } = 7.9019 \\ u_{0.05}^{* + } = 7.9030 \end{array}$ $\begin{array}{l} u_{0.01}^{* - } = 7.9017 \\ u_{0.01}^{* + } = 7.9032 \end{array}$ $\begin{array}{l} u_{0.001}^{* - } = 7.9015 \\ u_{0.001}^{* + } = 7.9034 \end{array}$ Lena 7.9024/7.9027 Pass Pass Pass Peppers 7.9027/7.9023 Pass Pass Pass DownLoad:
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测试图像 NPCR (gray/color) NPCR理论临界值 $N_{0.05}^* = 99.5693\% $ $N_{0.01}^* = 99.5527\% $ $N_{0.001}^* = 99.5341\% $ Lena 0.9960/0.9961 Pass Pass Pass Lena[12] 0.9954/— Fail Fail Pass Lena[20] 0.9962/— Pass Pass Pass Peppers 0.9959/0.9957 Pass Pass Pass Peppers[12] 0.9944/— Fail Fail Fail Peppers[20] 0.9963/— Pass Pass Pass DownLoad:
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测试图像 UACI (gray/color) UACI理论临界值 $\begin{array}{l} u_{0.05}^{* - } = 33.2824\% \\ u_{0.05}^{* + } = 33.6447\% \end{array}$ $\begin{array}{l} u_{0.01}^{* - } = 33.2255\% \\ u_{0.01}^{* + } = 33.7016\% \end{array}$ $\begin{array}{l} u_{0.001}^{* - } = 33.1594\% \\ u_{0.001}^{* + } = 33.7677\% \end{array}$ Lena 0.3352/0.3357 Pass Pass Pass Lena[12] 0.3303/— Fail Fail Fail Lena[20] 0.3370/— Fail Pass Pass Peppers 0.3333/0.3331 Pass Pass Pass Peppers[12] 0.3305/— Fail Fail Fail Peppers[20] 0.3369/— Fail Pass Pass DownLoad:
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图像大小 压缩重构(gray/color) 加解密(gray/color) 总时间(gray/color) 编程工具 平台 256 × 256 0.21/0.20 0.66/0.65 0.87/0.85 Pycharm + Pytorch i5-8500 CPU — 0.93/2.81 0.93/2.81 512 × 512 0.91/0.89 2.51/2.51 3.42/3.40 — 3.89/11.96 3.89/11.96 1024 × 1024 4.81/4.62 9.40/9.42 14.21/14.04 — 15.84/48.51 15.84/48.51 DownLoad:
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[1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22]
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