Virtual Conference Intrusion Detection Using Audio-Video ML
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Solution Overview
Problem
Conventional virtual conferencing platforms lack effective means to secure environments where participants join the conference, especially for sensitive discussions, as specialized facilities like SCIFs are expensive or logistically impractical, and existing security measures do not protect against physical intrusions.
Innovation Solution
Client devices use sensors and machine learning models to detect unauthorized persons or devices in the background, providing notifications and potentially disconnecting from the conference if intrusions are detected, ensuring secure environments without specialized facilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If specialized facilities like SCIFs are used to secure virtual conference environments, then security against physical intrusions is improved, but cost and logistical complexity increase significantly
Solution Approach 1:
The patent replaces physical security infrastructure (SCIFs) with electronic and software-based detection systems. Machine learning models analyze audio and video streams to detect intrusions, substituting mechanical/physical security measures with intelligent computational systems that can identify unauthorized persons, recording devices, and other security threats through pattern recognition in sensor data.
Solution Approach 2:
The system uses virtual copies and representations of the physical environment through audio and video streams. Instead of requiring participants to be physically present in secured facilities, the system creates digital representations of conference environments and analyzes these copies for security threats, enabling remote security monitoring without physical presence requirements.
2Reliability
If specialized facilities like SCIFs are used to secure virtual conference environments, then security against physical intrusions is improved, but cost increases significantly
Solution Approach 1:
The patent employs standard consumer-grade computing devices (laptops, smartphones, tablets) with built-in cameras and microphones instead of expensive specialized security infrastructure. These ubiquitous, relatively inexpensive devices leverage existing sensors and processing capabilities to perform security functions, dramatically reducing the cost barrier while maintaining security effectiveness through software intelligence.
Solution Approach 2:
The system enables each participant's device to autonomously monitor its own environment for security threats. The machine learning models run locally or with minimal cloud support, allowing devices to self-detect intrusions, unauthorized recording devices, and other security issues without requiring centralized expensive security infrastructure or constant external monitoring resources.
3Difficulty of detecting and measuring
If sensors and machine learning models are used to detect intrusions, then security monitoring capability is improved, but device complexity and processing requirements increase
Solution Approach 1:
The patent divides the security monitoring function into separate, specialized machine learning models that process different aspects of sensor data independently. One model analyzes video streams for unauthorized persons and devices, another processes audio streams for suspicious sounds or voices, and additional models may detect specific object types. This segmentation allows each component to remain relatively simple while the collective system achieves comprehensive detection capability.
Solution Approach 2:
The system uses multi-functional machine learning models that can detect multiple types of security threats using the same audio and video sensor inputs. A single integrated security monitoring system performs intrusion detection, identifies unauthorized recording devices, detects suspicious behaviors, and monitors environmental anomalies, replacing what would otherwise require multiple separate specialized systems.
Data Source
AI summary
One example method includes receiving, during a virtual conference hosted by a virtual conference provider, one or more audio or video streams from one or more client devices connected to the virtual conference, each client device associated with a participant attending the virtual conference; providing, to a trained machine learning (“ML”) model, the received one or more audio or video streams to determine a potential security intrusion; in response to receiving an indication of a potential security intrusion from the trained ML model: generating an indication of the potential security intrusion; and providing the indication to one or more client devices of the one or more client devices.