Application of face image detection based on deep learning in privacy security of intelligent cloud platform

Abstract

The application of deep learning-based face detection in the privacy and security of intelligent cloud platforms is studied, in order to resolve the risk of private photo leakage on such platforms. Five key feature points of human faces (two eyes, two noses and two corners of mouth) are sought using the multitask cascaded convolution network (MTCNN). The algorithm utilizes the intrinsic links among Proposal Network (P-Net), Refine Network (R-Net) and Output Network (O-Net) to improve their face detection performances substantially. Since many existing data encryption methods, such as DES, RSA and AES, are applicable only to test data instead of digital images, the encryption of eigenvalues is achieved with a combination of chaotic logic diagrams and RC4 stream ciphers. Meanwhile, the MTCNN-generated face coordinates and user passwords are hash-converted and double-encrypted using hash table. The results show that the face detection accuracy of MTCNN reaches 95.04%, and that the image encryption method is suitable for network transmission. The chaotic logic diagrams increase the security of S-box initialization in the RC4 algorithm. The hash structure accelerates the file reading, whereas the hash conversion improves the security of critical data. In conclusion, the proposed encryption scheme is computationally fast and highly secure.