AI Video Filtering Module for PII Removal
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Solution Overview
Problem
Current video technologies lack the capability to selectively and efficiently remove personally-identifiable information (PII) from video footage segments, requiring the deletion of entire segments to comply with privacy regulations, which is inefficient and may not meet increasing consumer and regulatory demands for data privacy.
Innovation Solution
A post-recording, pre-streaming filtering system that utilizes a machine-learning, artificially-intelligent filtering module to identify and delete PII from recorded videos before streaming, allowing for targeted removal of PII and compliance with privacy regulations, including user selection and historical data analysis for PII identification, and optional pixel modification watermarking for security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If entire video segments are deleted to remove PII, then privacy compliance is achieved, but video data loss increases and efficiency decreases
Solution Approach 1:
The system extracts and removes only the PII elements (faces, license plates, personal items) from video segments while preserving the rest of the video content. This selective extraction approach maintains privacy compliance without requiring deletion of entire video segments, thus reducing video data loss while achieving the same privacy protection goal.
Solution Approach 2:
The filtering module applies different processing quality to different regions of the video: PII regions are removed or obscured while non-PII regions are preserved in full quality. This local differentiation allows the system to maintain high overall video quality and minimize data loss while still achieving comprehensive privacy compliance.
2Measurement precision
If machine learning filtering is implemented, then PII removal precision improves, but device complexity increases
Solution Approach 1:
The system introduces a specialized filtering module as an intermediary component between video recording and storage/streaming. This module encapsulates the machine learning functionality in a dedicated unit, making the complex PII identification and removal process manageable and maintainable while achieving high identification accuracy through trained models.
Solution Approach 2:
The filtering module is configured with pre-trained machine learning models that have already learned to identify various types of PII. This preliminary training action allows the system to achieve high PII identification accuracy without requiring complex real-time training processes, thereby reducing operational complexity while maintaining precision.
3Reliability
If PII filtering is performed before streaming, then privacy security improves, but processing time increases
Solution Approach 1:
The system performs PII filtering in advance before video streaming or storage, so that privacy-protected video can be delivered immediately without real-time processing delays. This preliminary action ensures data security is established upfront while allowing efficient streaming afterward without ongoing processing time penalties.
Data Source
AI summary
Apparatus and methods are provided for post-recording, pre-streaming, pre-storing, video filtering and processing of personally-identifiable information (“PII”). The system may include a video recording device operable to record videos. The system may include a machine-learning, artificially-intelligent filtering module. The filtering module may receive recorded videos from the video recording device. The filtering module may analyze the recorded video to identify PII included in the recorded videos. The filtering module may delete the identified PII. The filtering module may enable streaming of the recorded video absent the PII. The system may include a streaming module. The streaming module may stream the video to a physical storage medium. The system may include a storage medium. The storage medium may receive the recorded streamed video absent the PII.