Endpoint Deepfake Detection With Real-Time Audio-Visual Analysis
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Solution Overview
Problem
Existing deepfake detection technologies are limited by requiring manual file uploads for analysis, lack integration into broader security frameworks, and are ineffective for real-time detection across multiple modalities, particularly in standalone applications outside web browsers.
Innovation Solution
A computing module that integrates real-time deepfake detection on endpoints, utilizing a deepfake visual detection model for facial images and an audio inverter model for audio analysis, capable of detecting manipulations across various platforms and applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If web-based platforms or APIs are used for deepfake detection, then deepfake analysis can be performed, but real-time detection capability is lost due to manual file upload requirements
Solution Approach 1:
The system performs preliminary actions by pre-processing audio segments and reconstructing original audio characteristics before detection. The audio inverter model pre-reverses transformations introduced by the operating system sound mixer, preparing the audio data in advance for rapid deepfake detection without requiring manual file uploads during the actual detection event.
Solution Approach 2:
The patent replaces the manual mechanical process of file upload and platform submission with an automated endpoint-based detection system. The computing module automatically captures audio segments, processes them through the audio inverter model, and performs detection locally, eliminating the need for users to manually upload files to web-based platforms.
2Adaptability or versatility
If real-time audio analysis is implemented in standalone applications, then detection coverage is improved, but integration complexity increases
Solution Approach 1:
The system achieves universality by designing the computing module to work across multiple platforms and applications. The audio inverter model and deepfake detection model are implemented as standalone components that can be integrated into various video call platforms and standalone applications, providing multi-modal detection capability without requiring platform-specific customization.
Solution Approach 2:
The detection system is segmented into independent functional modules: audio segment acquisition, audio pre-processing, audio inverter model for reconstruction, and deepfake detection model. This modular architecture reduces integration complexity by allowing each component to be developed and deployed independently while maintaining platform compatibility.
3Reliability
If point solutions are deployed for deepfake detection, then specific detection tasks can be performed, but integration into broader security frameworks is limited
Solution Approach 1:
The computing module is designed with universal functionality that enables integration into broader security frameworks. It provides comprehensive multi-modal detection (visual and audio) that can be embedded within existing security architectures, allowing organizations to integrate deepfake detection into their overall security ecosystem rather than operating as isolated point solutions.
Data Source
AI summary
This document describes a system and method for detecting deepfakes in real-time. In particular, the described system and method is configured to detect, in real-time, if a captured image and/or audio segment comprises a deepfake