About DocShield AI

Intelligent Document Forgery & Deepfake Detection Platform built for healthcare claims fraud prevention under AB PM-JAY Auto Adjudication Hackathon. Designed as an enterprise-grade forensic intelligence system for real-world deployment and scalable fraud governance.

System Overview

Mission

Prevent healthcare fraud by detecting forged, manipulated, AI-generated, and tampered medical documents before claim approval.

Vision

Build India’s most trusted forensic AI platform for secure, transparent, and fraud-proof healthcare claim verification.

Impact

Reduce fraudulent claims, improve trust, protect public funds, and strengthen healthcare governance.

System Architecture

01

Upload Layer

PDFs, scanned reports, discharge summaries, prescriptions, lab reports, radiology scans, and claim attachments enter through secure forensic intake.

02

AI Detection Engine

Forgery detection, deepfake identification, duplicate report matching, and metadata verification are executed automatically.

03

Forensic Visual Analyzer

Bounding boxes, suspicious region highlighting, overwritten text detection, and visual region inspection.

04

Explainable Intelligence

Evidence traceability, fraud reasoning, confidence scoring, and tampering explanation at page-level precision.

05

Reviewer Decision Engine

Approve, reject, conditional review, escalation recommendations, and secure audit reporting.

AI Models Used

OCR Intelligence

Text extraction, overwritten content detection, and hidden content verification using OCR + layout analysis.

Computer Vision Models

Forgery localization, suspicious region detection, and watermark removal analysis.

Deepfake Detection

Detection of fully AI-generated documents and partially AI-edited reports.

NLP Validation

Metadata consistency check, semantic anomaly detection, and structure validation.

Document DNA Engine

Font consistency, source authenticity, and cross-claim document fingerprinting.

Fraud Scoring Engine

Trust score generation, fraud probability, severity classification, and reviewer prioritization.

Deepfake Detection Pipeline

01

Document Intake

Claims and medical reports uploaded into secure forensic intake system.

02

Preprocessing

Noise cleanup, OCR extraction, metadata parsing, and page segmentation.

03

Tampering Detection

Signature forgery, stamp manipulation, overwritten text, and merged file detection.

04

Cross-Claim Matching

Duplicate report identification and provider fraud pattern matching.

05

Reviewer Decision

Final decision with explainable audit trail and forensic report generation.

Innovation Highlights

Document DNA Fingerprint

Unique forensic fingerprint generation for every uploaded healthcare document.

Cross-Document Fraud Matching

Identify duplicate and reused reports across hospitals and multiple claims.

Explainable AI Forensics

Every fraud decision includes reason tracing and confidence justification.

Built for Real-World Deployment

Not just a prototype — a scalable healthcare fraud prevention platform designed for production-grade adoption across hospitals, insurers, and public healthcare systems.

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