DeepTrust

Multimodal Trust Reasoning for Media Authenticity

Overview

DeepTrust is a multimodal forensic verification system designed to assess the authenticity of images, videos, and audio in real-world conditions. Instead of relying on a single model or opaque confidence score, DeepTrust reasons across multiple evidence streams and produces a calibrated, explainable trust verdict.

The system is built for scenarios where overconfidence is risky and explainability matters — such as journalism, content moderation, and digital forensics.

Key Capabilities:

Multimodal Analysis: Visual, structural (facial geometry), temporal (video), audio, and forensic signals analyzed in parallel

Multimodal Consistency Processor (MCP): Detects agreement or conflict across modalities and calibrates confidence accordingly

Visual Explainability: Attention heatmaps overlaid on face regions (eyes, mouth, cheeks) with severity indicators

Temporal & Audio Forensics: Frame-level anomaly timelines and audio spectrogram analysis

Robustness Testing: Evaluates stability under compression, noise, and degradation

Calibrated Trust Scoring: Produces Likely Authentic, Uncertain, or Likely Manipulated verdicts with evidence-backed explanations

Why DeepTrust Is Different?

Treats deepfake detection as a trust reasoning problem, not just classification

Explicitly models cross-modal disagreement instead of averaging scores

Exposes robustness and uncertainty, avoiding overconfident claims

Designed as a decision-support system, not a black-box detector

Tech Stack (High Level)

Frontend: React + TypeScript + Tailwind CSS

Backend: Supabase + Edge Functions

Analysis: Computer vision, audio forensics, structural graph analysis, explainability (Grad-CAM-style heatmaps)

Disclaimer

DeepTrust provides forensic signals and trust indicators, not legal proof. Results should be interpreted as decision support, especially in high-risk or legal contexts.