A Tri-modal Deepfake Forensics And Web Interception Architecture

Abstract

The Rapid Proliferation Of Highly Realistic Synthetic Media, Commonly Known As Deepfakes, Poses A Severe Threat To Digital Identity Verification And Media Authenticity. Current Deepfake Detection Methodologies Predominantly Rely On Single-modality Neural Networks Or Computationally Prohibitive Feature-level Fusion, Rendering Them Inefficient For Real-time Web Deployment. This Paper Surveys Existing Unimodal And Multimodal Deepfake Detection Frameworks And Proposes A Novel, Highly Scalable Alternative: A Decoupled, Tri-Modal Late-Fusion Architecture. The Proposed System Evaluates Media Through Three Parallel, Asynchronous Pipelines: A Spatial Engine Utilizing Error Level Analysis (ELA) Paired With A Convolutional Neural Network (CNN) For Compression Artifact Detection; A Biometric Engine Employing A ResNeXt-50 And LSTM Network For Temporal Facial Tracking; And An Auditory Engine Converting 1D Waveforms Into 2D Mel-Spectrograms For Synthetic Frequency Classification. By Intercepting Live WebRTC Streams Via A Zero-dependency DOM Injection Protocol, The Architecture Bypasses Traditional File-download Bottlenecks. Utilizing A Weighted Confidence Algorithm For Decision-level Fusion, The System Achieves A 97.8% Ensemble Accuracy And Gracefully Degrades In The Absence Of Specific Data Streams, Analyzing 5-second Media Buffers With A Maximum Latency Of 2.1 Seconds. This Survey Demonstrates That Decoupled, Parallel Modality Processing Offers A Vastly Superior, Fault-tolerant Framework For Commercial Deepfake Interception Compared To Traditional Synchronous Models.

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