Conference Media Trust Verification Against Deepfake Impersonation
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Solution Overview
Problem
The challenge of trusting whether a real person or a deepfaked version is participating in audio and/or video conferences poses a security risk, as existing systems lack effective methods to authenticate participants and provide real-time feedback on trust levels.
Innovation Solution
A communication system and method that determine and display trust levels based on media data from audio and video devices, using cryptographic keys, signatures, and biometric data to verify the authenticity of participants, and provide real-time feedback to users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional communication systems are used without trust verification, then the system complexity remains low and ease of operation is maintained, but security and reliability deteriorate due to susceptibility to deepfake attacks and impersonation
Solution Approach 1:
The system performs preliminary verification of media streams by checking cryptographic signatures and biometric data before establishing trust. This advance verification prevents impersonation attacks without requiring complex real-time analysis during communication, as the trust assessment is prepared in advance through pre-established cryptographic credentials.
Solution Approach 2:
The system introduces an intermediary trust assessment mechanism that mediates between communication parties. This intermediary layer verifies media streams through cryptographic validation and biometric matching, providing security without requiring direct complex interactions between participants, thus maintaining ease of operation while improving reliability.
2Loss of information
If real-time trust level monitoring is implemented, then security and awareness are improved, but information processing requirements and system complexity increase
Solution Approach 1:
The system implements feedback by continuously monitoring media streams and providing real-time trust level updates to participants. This feedback mechanism maintains trust awareness without requiring complex centralized control, as each participant independently assesses trust levels based on verified cryptographic and biometric data from incoming streams.
Solution Approach 2:
The system enables self-service trust verification where each communication participant independently validates media streams using pre-distributed cryptographic keys and biometric templates. This distributed self-verification approach reduces central processing requirements while maintaining real-time trust awareness across all participants.
Data Source
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AI summary
An electronic device and a method of operating an electronic device comprising processor circuitry is disclosed, the method comprising receiving media data associated with a media stream, the media data comprising one or both of audio data representative of audio and video data representative of video; determining a trust level associated with the media stream based on the media data; and providing an output indicative of the trust level.