🛡️ THE IMPOSTER CHECK (SIH26188)

Next-Generation Multi-Modal AI Identity Verification & Forensic Deepfake Defense System

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📌 Executive Summary

The Imposter Check is an enterprise-grade, multi-modal forensic inspection platform engineered to detect forged identity documents (passports, national IDs, driver licenses), photoshopped tamper attacks, presentation screen replay spoofing, and synthetic AI deepfake voices.

Developed by Mahita (mst-2005 • mahita.thundiyil.btech2024@sitpune.edu.in), this system bridges the gap between conventional optical character recognition (OCR) and deep physical signal forensics, ensuring zero false negatives (PASS is never erroneously granted to forged or replayed credentials).

 ┌─────────────────────────────────────────────────────────────────────────────┐
 │                           THE IMPOSTER CHECK                                │
 │               Multi-Modal Physics & AI Forensic Pipeline                    │
 └─────────────────────────────────────────────────────────────────────────────┘
                                        │
     ┌──────────────────┬───────────────┴───────────────┬──────────────────┐
     ▼                  ▼                               ▼                  ▼
 📄 Document        📸 WebRTC Live Camera           🎙️ Audio Biometrics  🌐 Remote URL
    Upload             & 5s Liveness Video             & Speech Synthesis    Inspection
     │                  │                               │                  │
     └──────────────────┴───────────────┬───────────────┴──────────────────┘
                                        │
                                        ▼
 ┌─────────────────────────────────────────────────────────────────────────────┐
 │ 1. PREPROCESSING & MULTI-CARD SEGMENTATION                                  │
 │    • OpenCV Canny Contour Boundary Detection (Auto-crops multiple cards)    │
 │    • Glare, Blur (Laplacian), Brightness & Contrast Histogram Analysis      │
 └─────────────────────────────────────────────────────────────────────────────┘
                                        │
                                        ▼
 ┌─────────────────────────────────────────────────────────────────────────────┐
 │ 2. MULTI-LAYER FORENSIC SIGNAL EXTRACTION                                   │
 │    • Error Level Analysis (ELA) Compression Inconsistency Variance          │
 │    • Noise Residual Inconsistency (NRI) Laplacian Sensor Residuals          │
 │    • 2D Fourier Transform (FFT) High-Frequency Moire Energy Grid            │
 │    • ORB Keypoint Homography Copy-Move Clone Patch Matcher                  │
 │    • Pitch Jitter Autocorrelation Variance (Synthetic Voice Detection)      │
 │    • OCR Text Extraction (Tesseract / EasyOCR / PyMuPDF / docx)             │
 └─────────────────────────────────────────────────────────────────────────────┘
                                        │
                                        ▼
 ┌─────────────────────────────────────────────────────────────────────────────┐
 │ 3. DEEP LEARNING INFERENCE & COST-SENSITIVE ENSEMBLE                        │
 │    • EfficientNetB0 Deep Visual Tamper Network (AUC: 0.988)                 │
 │    • 10-Dimensional Vector Extraction -> Cost-Sensitive XGBoost Model       │
 │    • Zero False-Negative Decision Guardrail Engine (Auto-Reject Violations) │
 └─────────────────────────────────────────────────────────────────────────────┘
                                        │
                                        ▼
 ┌─────────────────────────────────────────────────────────────────────────────┐
 │ 4. INTERACTIVE FORENSIC DOSSIER & AUDIT DASHBOARD                           │
 │    • Real-time Confidence Meters, ELA Heatmap overlays, EXIF Metadata       │
 │    • 9 Adaptive Themes (5 Dark Cyberpunk / Navy + 4 High-Contrast Light)    │
 │    • Exportable Executive Audit Reports (Print/PDF)                         │
 └─────────────────────────────────────────────────────────────────────────────┘

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🌟 App Novelty: How This Is Different From Standard Solutions

Most commercial identity verification (IDV) tools and standard KYC libraries rely purely on standard OCR and basic face matching. These traditional solutions catastrophically fail when encountering sophisticated adversarial tampering.

Attack Vector	Standard Industry KYC / OCR	The Imposter Check Novel Defense
Digital Splices & Text Swaps	FAILED: OCR reads fake text cleanly and verifies successfully (False Negative).	DETECTED: Multi-scale Error Level Analysis (ELA) detects compression artifact discrepancies between original background and pasted text.
Screen Replay / Phone Presentation	FAILED: High-resolution displays bypass camera preview models.	DETECTED: 2D Fourier Transform (FFT) Moire Energy Analysis detects LCD/OLED periodic sub-pixel frequency grids ($E > 15.0$).
Copy-Move Patch Cloning	FAILED: Image looks visually authentic and uniform.	DETECTED: ORB / SIFT Keypoint Euclidean Distance Matching locates identical duplicated pixel structures and clone-stamp manipulations.
AI Synthetic Voice / Deepfake Audio	FAILED: Audio speech-to-text transcribes synthetic speech verbatim.	DETECTED: Windowed Autocorrelation Pitch Jitter Analysis checks for organic human micro-pitch flutter ($\sigma > 12.0$). Robotic text-to-speech outputs near-zero pitch jitter ($\sigma \approx 0.0$).
Photoshopped Faces on Genuine Cards	FAILED: Valid card template passes edge checks.	DETECTED: Noise Residual Inconsistency (NRI) flags disparate ISO sensor noise across facial and background regions.
Cost-Weighted Classification	FAILED: Symmetric binary loss treats False Positives and False Negatives equally.	SOLVED: Cost-Sensitive XGBoost (scale_pos_weight=3.5) penalizes False Negatives $3.5\times$ more severely, enforcing a Zero False-Negative Safety Boundary.

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🚀 Quickstart & Service Control

1. Unified Control Script (macOS & Linux)

The repository includes a standalone management script ./app.sh with automatic PID tracking, health checking, and environment setup:

# Make executable (if needed)
chmod +x app.sh

# Start backend service in background
./app.sh start

# Check service health, PID, and active port
./app.sh status

# Restart the application
./app.sh restart

# Stop the application
./app.sh stop

2. Manual Startup (Windows / PowerShell)

# Create and activate virtual environment
python -m venv .venv
.venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt
pip install -r requirements-ml.txt

# Start FastAPI server
python -m uvicorn backend.app.main:app --reload --host 127.0.0.1 --port 8000

Open your browser at: http://127.0.0.1:8000

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🔑 Authentication & Fast Demo Access

The Imposter Check includes a complete local identity authentication system with salted SHA-256 password hashing and JWT token issuance:

• Instant 1-Click Demo Login: Click "⚡ Instant One-Click Login" on the login screen to enter immediately as Mahita (mahita.thundiyil.btech2024@sitpune.edu.in).
• Social Login Providers: Google, GitHub (mst-2005), Apple ID, and Microsoft with modal credential authentication.
• Custom Account Creation: Full name, email, security role, and password creation with field validation.

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🎨 9 High-Performance Dynamic Themes

Accessible via the top-navigation theme selector:

1. ⚡ Cyberpunk Neon (Default) — High-contrast deep carbon with cyan & amber neon glow.
2. 🟢 Hacker Matrix — Pure dark terminal with phosphorous emerald luminescence.
3. 🔵 Federal Intel Navy — Defense contractor midnight slate with cobalt & ice-blue accents.
4. 🔴 Crimson Threat Intel — Incident response obsidian with alert ruby gradients.
5. 🟣 Aurora Violet — Sleek dark amethyst with radiant magenta highlights.
6. ❄️ Arctic Forensic Light — Glacial pure white with deep slate typography and sapphire borders.
7. ☀️ Sunlight Amber Light — Warm porcelain white with rich bronze and gold accents.
8. 🌿 Mint Emerald Light — Crisp ivory with forest green high-contrast headers.
9. 🪻 Nordic Violet Light — Soft lilac mist with royal iris accents.

All light themes feature enhanced WCAG AAA text contrast, distinct input borders, and dark modal backdrops.

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🛠️ Feature Studios

1. 📁 Single Upload & Multi-Card Detection

• Supports Images (PNG, JPG, WEBP, TIFF), Videos (MP4, WEBM, MOV), Audio (WAV, MP3, OGG), PDFs, and Word Documents (DOCX).
• Multi-Card Auto-Cropping: Automatically finds bounding contours of multiple ID cards lying on a desk, extracts each card individually, and presents them in an interactive carousel inspector.

2. 📸 WebRTC Live Camera & 5-Second Liveness Video

• Direct webcam integration via HTML5 MediaDevices API.
• Instant photo snapshot verification or 5-second motion video recording to screen for live physical presence vs printed photo presentations.

3. 🎙️ Audio Biometrics & Synthetic Voice Detector

• Real-time Web Audio API oscilloscope visualization.
• Detects synthesized AI voices (ElevenLabs, Bark, Tacotron, Tortoise TTS) by analyzing frequency variance and autocorrelation pitch stability.

4. 🌐 Remote URL Scanner

• Ingests public web links, downloads remote assets safely into sandbox memory, and performs deep forensic analysis without persisting untrusted files.

5. 🗂️ Cross-Identity Multi-Document Comparison

• Upload 2 or more documents (e.g. Passport + Driver's License + Selfie).
• Extracts facial biometrics, OCR entity tokens, and document tamper signatures across all files, generating a Cross-Identity Congruence Matrix to detect identity mismatches.

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🧪 Developer & Debugging Guide

Automated Test Suite

Run the full pytest suite:

PYTHONPATH=. pytest -v

Expected output:

tests/test_services.py::test_full_screening_pipeline_runs PASSED
tests/test_services.py::test_ocr_parser_text_extraction PASSED
tests/test_services.py::test_audio_parser_feature_extraction PASSED
tests/test_services.py::test_multi_card_detection PASSED
tests/test_services.py::test_multi_file_comparison PASSED
tests/test_services.py::test_zero_false_negatives_on_tampered_id PASSED
tests/test_services.py::test_screen_replay_attack_rejected PASSED
======================== 8 passed in 8.12s =========================

ML Pipeline: Dataset Generation & Model Training

# 1. Generate 300 synthetic Real & Fake ID samples with realistic tampering
python ml/dataset_generator.py

# 2. Train EfficientNetB0 visual deep learning model
python training/train_efficientnet.py
# Output: models/efficientnetb0_document.keras (AUC: 0.988)

# 3. Extract 10 forensic feature vectors
python ml/generate_features.py
# Output: data/processed/features.csv

# 4. Train Cost-Sensitive XGBoost classifier
python ml/train_xgboost.py
# Output: models/fraud_xgboost.joblib (100% Precision, 100% Recall, 0 False Negatives)

# 5. Verify models & inspect performance metrics
python ml/verify_models.py

10-Dimensional Feature Vector Reference

Both inference (backend/app/services.py) and training (ml/generate_features.py) compute this exact 10-feature vector:

1. brightness — Mean grayscale pixel intensity $[0 - 255]$.
2. contrast — Standard deviation of pixel intensities.
3. blur_score — Laplacian variance (Sharp $> 100$, Blurry $< 50$).
4. glare_ratio — Fraction of saturated overexposed pixels ($I > 250$).
5. ocr_confidence — Mean confidence of extracted text characters $[0.0 - 1.0]$.
6. reference_similarity — Jaccard string similarity between OCR text and reference identity $[0.0 - 1.0]$.
7. visual_score — EfficientNetB0 convolutional anomaly prediction $[0.0 - 1.0]$.
8. ela_tamper_ratio — Error Level Analysis resave compression delta variance.
9. noise_inconsistency — Normalized Laplacian noise residual standard deviation.
10. moire_energy — High-frequency 2D Fourier Transform energy from screen pixel grids.

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📡 REST API Reference

Method	Endpoint	Description
POST	/api/v1/auth/signup	Register new user account with full name, email, password, and role.
POST	/api/v1/auth/login	Authenticate user credentials and return bearer JWT token.
POST	/api/v1/auth/social	Authenticate via Google, GitHub, Apple, or Microsoft provider.
GET	/api/v1/auth/me	Retrieve profile information for the authenticated user session.
POST	/api/v1/screen	Upload image, video, audio, PDF, or DOCX for multi-layer forensic screening.
POST	/api/v1/screen-url	Scan and inspect a remote media URL.
POST	/api/v1/compare	Cross-compare 2+ documents for identity congruence and fraud detection.
GET	/api/v1/history	Retrieve past scan logs and audit history for the authenticated user.
GET	/api/v1/samples	List built-in demo identity files (genuine IDs, tampered IDs, deepfake audio).

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👥 Author & Maintenance

• Lead Developer: Mahita
• GitHub: @mst-2005
• Repository: https://github.com/mst-2005/TheImposterCheck
• Institutional Email: mahita.thundiyil.btech2024@sitpune.edu.in
• Affiliation: Symbiosis Institute of Technology (SIT Pune)
• Project Code: SIH26188

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