Media-Based User Authentication Using Temporal Risk Analysis
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Solution Overview
Problem
Conventional user authentication systems are vulnerable to malicious attacks and hacking, as they rely on static credentials and images, which can be easily deceived by fraudulent means, leading to security breaches and data loss.
Innovation Solution
A system that evaluates multiple digital media recordings over time to detect features such as background noises, objects, and natural light, using AI models to generate risk metrics and determine the probability of user authenticity, thereby enhancing security by verifying the user's presence through dynamic media analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional authentication systems use static credentials and images, then the authentication process is simple and fast, but the system becomes vulnerable to malicious attacks and hacking
Solution Approach 1:
The patent transforms static authentication credentials into dynamic media recordings captured at different times. The system analyzes video and audio recordings that change over time, evaluating features such as background noises, objects, and natural light variations. This dynamic approach prevents use of stolen or fabricated credentials while maintaining a manageable authentication process through automated AI analysis.
Solution Approach 2:
The system performs preliminary analysis of media recordings by evaluating multiple features (background noises, objects, natural light) before making an authentication determination. AI models pre-process the media content to generate risk metrics, allowing the system to identify potential fraud attempts before final authentication decisions are made, thereby enhancing security without significantly increasing user-facing complexity.
2Measurement precision
If the system requests multiple media recordings over different times, then authentication accuracy improves, but the authentication process takes longer
Solution Approach 1:
The system implements periodic authentication challenges where media recordings are requested at different time intervals. By analyzing recordings captured at multiple distinct times, the system builds a temporal profile of the user's environment and behavior patterns. This periodic sampling increases authentication accuracy by making it difficult for attackers to replicate consistent environmental features across different time periods.
Solution Approach 2:
The patent replaces manual review of multiple media recordings with automated AI models that rapidly analyze video and audio content. The AI systems automatically evaluate features such as background noises, objects, and natural light, generating risk metrics without human intervention. This substitution of mechanical/AI processing for manual review maintains high authentication accuracy while significantly reducing the time required to process multiple recordings.
3Reliability
If the system evaluates multiple features in media recordings, then the ability to detect fraudulent attempts improves, but the computational resources required increase
Solution Approach 1:
The authentication system segments the analysis of media recordings into distinct feature evaluation modules. Different AI models or analysis components handle specific features such as background noises, objects, and natural light separately. This segmentation allows the system to process multiple features in parallel, distributing computational load efficiently and reducing peak energy consumption while maintaining comprehensive fraud detection capabilities.
Solution Approach 2:
The system implements a risk-based approach where AI models evaluate features to generate risk metrics, but not all features are analyzed with equal depth for every authentication attempt. The system may adjust the level of analysis based on initial screenings, user risk profiles, or authentication context, performing more extensive feature evaluation only when necessary. This partial action approach maintains high fraud detection capability while optimizing computational energy consumption by avoiding unnecessary exhaustive analysis in low-risk scenarios.
Data Source
AI summary
Systems and methods for digital media-based user authentication are disclosed herein. In some aspects, the system may receive an authentication request. The system may transmit a user verification request based on the authentication request. The system may receive media recordings in response to the user verification request. The system may detect one or more features in the media recordings. The system may generate a plurality of risk metrics for the user based on generating risk metrics for the one or more features. The system may determine an authentication probability based on the plurality of risk metrics.