Secret Image Sharing Schemes Based on Region Convolution Neural Network

Digital image has become an important information carrier in the era of rapid network development, the security protection of image information has also become an important research topic in the security field. Secret image sharing is a threshold based approach that can protect confidential information in an image among multiple users. This scheme encrypts the secret image into several shadow images according a threshold and distributes them to different users. When the number of users reaches the threshold, the original image can be reconstructed, otherwise the user cannot obtain any information about the original image. The classification and recognition of image information is the premise and basis for image secret sharing, CNN (convolutional neural network) has higher accuracy and faster speed in image classification and recognition. In this paper, we combine CNN based image recognition and classification with secret image sharing together to applying the tool of deep learning in the field of information protection. First, we adopt Faster RCNN (region convolutional neural network) model to segment a secret image into multiple regions, where each region has dierent level, then progressive secret image sharing and secret image sharing with essential shadows are constructed, where the region with higher importance level needs higher threshold in reconstruction, this feature makes the image secret sharing scheme suitable for more application scenarios Compared with traditional image recognition methods based on artificial features, the use of Faster RCNN can greatly improve the efficiency of image classification and recognition, thereby further enhancing the application value of image secret sharing.