Video Feed Masking for Sensitive Object Privacy Protection
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Solution Overview
Problem
The lack of robust security protocols and encryption standards in digital interactions, particularly in video conferencing applications, leads to vulnerabilities that can be exploited by cybercriminals, compromising personal information and financial data, and posing risks of identity theft and fraudulent activities.
Innovation Solution
Implementing a system with an additional layer of security that uses adaptive machine learning to detect and respond to security threats by masking sensitive objects in video feeds, performing an initial check of the user's environment, and complying with data protection regulations to protect personal information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional video conferencing applications are used without additional security layers, then ease of operation is maintained, but user data privacy and security are compromised
Solution Approach 1:
The patent implements a nested architecture where a microservice is embedded within the video conferencing application. The microservice contains object detection models that are further nested within the service logic. This nested structure allows security functionality to be integrated without requiring a complete system redesign, maintaining operational simplicity while enhancing security through layered protection mechanisms.
Solution Approach 2:
The patent introduces an intermediary microservice that acts as a mediator between the video conferencing application and security protocols. This intermediary handles object detection, sensitivity determination, and masking operations, isolating the complexity from both the application layer and the user interface. The intermediary translates security requirements into actionable processing steps without requiring users to understand or configure complex security settings.
2Reliability
If adaptive machine learning is implemented for real-time threat detection, then security reliability is improved, but processing time and computational resources increase
Solution Approach 1:
The patent implements preliminary action by pre-training object detection models with diverse datasets before deployment. The microservice is pre-configured with multiple trained models that can immediately process video feeds without requiring training during runtime. This preliminary preparation enables real-time detection while avoiding the time-consuming training process during actual security operations.
Solution Approach 2:
The patent applies partial action by implementing selective object detection and masking. Rather than processing or masking the entire video feed, the system detects only specific sensitive objects (PII, financial data, sensitive items) and applies masking only to those regions. This partial processing approach maintains security effectiveness while significantly reducing computational overhead and processing time compared to full-frame analysis.
3Reliability
If sensitive objects are masked in video feeds, then data privacy is protected, but information transmission completeness is reduced
Solution Approach 1:
The patent implements local quality by applying different processing treatments to different regions of the video feed. Sensitive objects are identified and masked with blur or pixelation effects applied only to those specific locations, while the rest of the video feed remains clear and unobstructed. This localized approach protects privacy where needed while preserving the completeness and usability of the overall video transmission.
Solution Approach 2:
The patent uses color changes and visual transformations (blurring, pixelation, overlaying) to mask sensitive objects. These visual modifications alter the appearance of protected regions without completely removing or deleting the underlying data, maintaining a balance between privacy protection and information retention. The masking techniques preserve spatial context and video continuity while preventing unauthorized viewing of sensitive content.
Data Source
AI summary
A method for managing security of a user includes: establishing a connection with a visual sensor; capturing a snapshot of an environment, in which the snapshot is a viewable area of the environment that can be imaged by the visual sensor; making a first determination that an image of a sensitive object (SO) is detected in the environment; protecting the SO by blurring or masking the image; making a second determination that the user joins a call; monitoring, based on the second determination, the environment to capture a video feed; making, based on the feed, a third determination that a sensitive action is detected; protecting, based on the third determination, a second SO (SSO) resulting from the sensitive action by blurring or masking a second image of the SSO; and making a fourth determination that the user requests removal of the blurring or the masking of the second image.