With the advancement of video recording devices and network infrastructure,we use surveillance cameras to protect our valuable assets.This paper proposes a novel system for encrypting personal information within recor...With the advancement of video recording devices and network infrastructure,we use surveillance cameras to protect our valuable assets.This paper proposes a novel system for encrypting personal information within recorded surveillance videos to enhance efficiency and security.The proposed method leverages Dlib’s CNN-based facial recognition technology to identify Regions of Interest(ROIs)within the video,linking these ROIs to generate unique IDs.These IDs are then combined with a master key to create entity-specific keys,which are used to encrypt the ROIs within the video.This system supports selective decryption,effectively protecting personal information using surveillance footage.Additionally,the system overcomes the limitations of existing ROI recognition technologies by predicting unrecognized frames through post-processing.This research validates the proposed technology through experimental evaluations of execution time and post-processing techniques,ensuring comprehensive personal information protection.Guidelines for setting the thresholds used in this process are also provided.Implementing the proposed method could serve as an effective solution to security vulnerabilities that traditional approaches fail to address.展开更多
基金supported by the Institute of Information and Communications Technology Planning and Evaluation (IITP)funded by the Korea Government (MIST),Development of Collection and Integrated Analysis Methods of Automotive Inter and Intra System Artifacts through Construction of Event-Based Experimental System,under RS-2022-II221022.
文摘With the advancement of video recording devices and network infrastructure,we use surveillance cameras to protect our valuable assets.This paper proposes a novel system for encrypting personal information within recorded surveillance videos to enhance efficiency and security.The proposed method leverages Dlib’s CNN-based facial recognition technology to identify Regions of Interest(ROIs)within the video,linking these ROIs to generate unique IDs.These IDs are then combined with a master key to create entity-specific keys,which are used to encrypt the ROIs within the video.This system supports selective decryption,effectively protecting personal information using surveillance footage.Additionally,the system overcomes the limitations of existing ROI recognition technologies by predicting unrecognized frames through post-processing.This research validates the proposed technology through experimental evaluations of execution time and post-processing techniques,ensuring comprehensive personal information protection.Guidelines for setting the thresholds used in this process are also provided.Implementing the proposed method could serve as an effective solution to security vulnerabilities that traditional approaches fail to address.
