With the rapid development of communication technology, digital technology has been widely used in all walks of life. Nevertheless, with the wide dissemination of digital information, there are many security problems. Aiming at preventing privacy disclosure and ensuring the safe storage and sharing of image and video data in the cloud platform, the present work proposes an encryption algorithm against neural cryptography based on deep learning. Primarily, the image saliency detection algorithm is used to identify the significant target of the video image. According to the significant target, the important region and nonimportant region are divided adaptively, and the encrypted two regions are reorganized to obtain the final encrypted image. Then, after demonstrating how attackers conduct attacks to the network under the ciphertext attack mode, an improved encryption algorithm based on selective ciphertext attack is proposed to improve the existing encryption algorithm of the neural network. Besides, a secure encryption algorithm is obtained through detailed analysis and comparison of the security ability of the algorithm. The experimental results show that Bob’s decryption error rate will decrease over time. The average classification error rate of Eve increases over time, but when Bob and Alice learn a secure encryption network structure, Eve’s classification accuracy is not superior to random prediction. Chosen ciphertext attack-advantageous neural cryptography (CCA-ANC) has an encryption time of 14s and an average speed of 69mb/s, which has obvious advantages over other encryption algorithms. The self-learning secure encryption algorithm proposed here significantly improves the security of the password and ensures data security in the video image.
This study aims to improve the efficiency and accuracy of image segmentation, and to compare and study traditional threshold-based image segmentation methods and machine learning model-based image segmentation methods. The krill herb optimization algorithm is combined with the traditional maximum between-class variance function to form a new graph segmentation algorithm. The pet dataset is used to train the algorithm model and build an image semantic segmentation system. The results show that when the traditional Ostu algorithm performs image single-threshold segmentation, the number of iterations is about 256. When double-threshold segmentation is performed, the number of iterations increases exponentially, and the execution time is about 2 s. The number of iterations of the improved Krill Herd algorithm in single-threshold segmentation is 6.95 times, respectively. The execution time for double-threshold segmentation is about 0.24 s. The number of iterations is only improved by a factor of 0.19. The average classification accuracy of the Unet network model and the SegNet network model is 86.3% and 91.9%, respectively. The average classification accuracy of the DC-Unet network model reaches 93.1%. This shows that the proposed fusion algorithm has high optimization efficiency and stronger practicability in multithreshold image segmentation. The DC-Unet network model can improve the image detail segmentation effect. The research provides a new idea for finding an efficient and accurate image segmentation method.
To improve the security of the Wireless Sensor Network (WSN), the image encryption algorithm is studied. Firstly, the shortcomings of current image encryption algorithms are compared. Secondly, based on the chaotic system, the color image encryption algorithm of double chaotic cross-diffusion is built, and the encryption platform is constructed in combination with the current development of intelligent equipment. Finally, the performance of the constructed algorithm and platform is evaluated, and the correlation of adjacent pixel pairs in different color channels and different directions of image encryption is verified. The results indicate that in the designed image encryption platform, the channels of different color are encrypted in different directions, and the encryption effect is good. Compared with the evaluation platform, the practicability of the encryption algorithm is good. After the image is encrypted, the correlation between adjacent pixels is significantly reduced. The image is highly correlated with adjacent pixels in three directions in the first three channels of encryption. After the studied algorithm is encrypted, the correlation coefficient value of adjacent pixel pairs is greatly reduced, and the maximum reduction is 0.97. The information entropy of the red, green, and blue channels before encryption is lower than the information entropy of the three-color channels after encryption. The information entropy of the algorithm is closer to the optimal value of the information entropy. For the index values of Unified Average Changing Intensity (UACI) and Number of Pixels Change Rate (NPCR), the average UACI of the algorithm is 99.62%, while the average of the UACI of the literature algorithm is 98.33%. The NPCR value of the algorithm is 33.52%, while the NPCR value of the literature algorithm is 31.23%. The encryption and decryption time of the algorithm is shorter than that of the literature algorithm. The constructed algorithm has high efficiency of encryption and decryption. It provides a reference for the enhancement of the security of image data in the WSN.
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