Digital Human Authentication Using Validation Data Against Deepfakes
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Solution Overview
Problem
Existing image generation and animation technologies can create highly realistic animated imagery that can deceive viewers, posing risks for nefarious purposes such as impersonation and fraud.
Innovation Solution
Generate predetermined validation data from a known individual, including mocap data, hair and skin models, and audio data, to analyze video streams and determine the authenticity of the depicted individual, providing a confidence level on the video's origin.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image generation and animation technologies are used to create realistic animated imagery, then the visual quality and realism of the imagery are improved, but the risk of impersonation and fraud increases
Solution Approach 1:
The system performs preliminary actions by capturing validation data (images, audio, motion capture) from the real individual before any potential impersonation attempts. This pre-collected data is stored and later used to authenticate video streams, preventing fraud by having the authentic reference data ready in advance for comparison
Solution Approach 2:
The system introduces an intermediary authentication mechanism that acts as a mediator between the video stream and the viewer. This intermediary analysis compares the video stream against validation data and provides authentication results, preventing direct deception by inserting a verification layer
2Measurement precision
If comprehensive validation data is collected from a known individual for authentication purposes, then the accuracy of authentication is improved, but the complexity of the system increases
Solution Approach 1:
The system segments the authentication process into distinct components: image validation data, audio validation data, and motion capture validation data. Each type of validation data is processed and analyzed separately, then combined to provide comprehensive authentication. This segmentation manages complexity by breaking down the overall system into manageable, specialized modules
Solution Approach 2:
The validation data collection system is designed to be universal and multi-functional, capturing multiple types of data (visual, audio, motion) that can be used for various authentication scenarios. This multi-functionality improves authentication accuracy across different use cases while consolidating the system into a single versatile platform rather than requiring separate systems for each data type
3Reliability
If analysis of video streams is performed to determine authenticity, then the ability to detect deepfakes is improved, but the processing time increases
Solution Approach 1:
The system performs partial analysis by focusing on key authentication indicators from the validation data and video stream rather than analyzing every pixel and frame in exhaustive detail. This selective analysis approach maintains deepfake detection capability while reducing processing time by concentrating computational resources on the most discriminative features
Data Source
AI summary
A video stream that depicts at least the face of an individual, and information identifying a known individual is received. Predetermined validation data derived from the known individual is accessed. An analysis of a segment of the video stream based on the predetermined validation data is performed. Based on the analysis, an output signal indicative of a confidence level that the video stream is a video stream generated by the known individual is provided.