Privacy Protection in Surveillance Videos Using Human Skin Encryption with Efficient Feedback Mechanism
Abstract
This paper presents an optimized privacy protection framework designed to enhance image security in video surveillance, addressing key challenges such as de-identification, compressibility, recoverability, and preservation within a unified architecture. The proposed approach introduces a hybrid system combining advanced human skin detection and encryption techniques to safeguard sensitive information under varying lighting and environmental conditions. The methodology operates in two key phases: skin detection and encryption. In the first phase, a Discriminative Skin Detection Approach (DSDA) is employed to identify human skin regions accurately. This approach leverages textural and spatial variables to enhance the classification of skin types, ensuring precise detection. An Enhanced Cipher Feedback Module Encryption (ECFME) encrypts the detected skin regions in the second phase. The Modified Golf Optimization Algorithm (MGOA) optimizes the encryption process, ensuring optimal vital parameters are selected for robust encryption. The input image undergoes preprocessing using a Gaussian filter to eliminate noise before proceeding to the detection and encryption stages. The methodology is implemented in MATLAB, and its performance is evaluated using comprehensive metrics. Comparative analysis demonstrates that the proposed approach outperforms conventional accuracy, efficiency, and privacy preservation techniques. This study contributes significantly to the field of privacy protection in video surveillance, offering a reliable and efficient solution for safeguarding sensitive visual data.