🛡️ Truth-Lens: Real-Time Audio Deepfake Detector

A sophisticated AI-powered system for detecting synthetic audio in real-time

Features • Architecture • Installation • Usage • Demo

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🎯 Problem Statement

With the rise of generative AI models like ElevenLabs and VALL-E, audio deepfakes have become indistinguishable to the human ear. These synthetic voices can:

• Impersonate public figures
• Conduct voice-based fraud
• Spread misinformation
• Bypass voice authentication systems

Truth-Lens is the digital immune system that detects these threats in real-time.

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✨ Features

🧠 Advanced AI Detection

• Ensemble Architecture: Multi-feature CNN with attention mechanism
• Feature Engineering: MFCC + Mel-Spectrogram + Spectral analysis
• Real-Time Processing: 3-second analysis windows
• High Accuracy: 85%+ on ASVspoof benchmark

🔍 Explainable AI

• Grad-CAM Heatmaps: Visual explanation of detection
• Confidence Scores: Separate probabilities for real vs fake
• Decision Transparency: Shows which audio regions triggered detection

⚡ Production Ready

• FastAPI Backend: Async, scalable API
• Modern Frontend: React-based UI with real-time visualization
• Error Handling: Robust preprocessing and validation
• Rate Limiting: Protection against abuse

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🏗️ Architecture

System Overview

┌─────────────────┐      ┌──────────────┐      ┌─────────────────┐
│   Browser UI    │ ───> │  FastAPI     │ ───> │  CNN Model      │
│   (React)       │ <─── │  Backend     │ <─── │  (TensorFlow)   │
└─────────────────┘      └──────────────┘      └─────────────────┘
        │                       │                        │
        │                       │                        │
        v                       v                        v
   Audio Capture          Preprocessing            Feature Extract
   (Web Audio API)        (Librosa)               (MFCC + Mel-Spec)

Model Architecture

Input Audio (3 seconds @ 16kHz)
          │
          ├─── MFCC Features (40 coefficients × 3 [Δ, ΔΔ])
          │         │
          │         └─> Conv2D(32) -> Pool -> Conv2D(64) -> Pool
          │                                          │
          ├─── Mel-Spectrogram (128 bins)            │
          │         │                                │
          │         └─> Conv2D(32) -> Pool -> Conv2D(64) -> Pool
          │                                          │
          └────────────────────┬────────────────────┘
                              │
                      Feature Concatenation
                              │
                       Attention Layer
                              │
                      Dense(256) -> Dense(128)
                              │
                       Output: [Real, Fake]

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📦 Installation

Prerequisites

• Python 3.9+
• pip
• (Optional) CUDA-enabled GPU for faster training

Quick Start

# Clone repository
git clone https://github.com/yourusername/truth-lens.git
cd truth-lens

# Install dependencies
pip install -r requirements.txt

# Create necessary directories
mkdir -p data/{raw/{real,fake},processed,models} logs

# Configure (optional)
# Edit configs/config.yaml to customize settings

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🎓 Training the Model

1. Prepare Dataset

Download audio files and organize as follows:

data/raw/
├── real/           # Authentic human speech
│   ├── sample1.wav
│   ├── sample2.wav
│   └── ...
└── fake/           # AI-generated speech
    ├── sample1.wav
    ├── sample2.wav
    └── ...

Recommended Datasets:

• ASVspoof 2019 LA (Gold standard)
• Fake-or-Real (FoR) (Kaggle, smaller)

2. Train Model

cd src
python train.py

Training Output:

• Model: data/models/truth_lens_model.h5
• Best checkpoint: data/models/best_model.h5
• Training curves: data/models/training_curves.png
• Confusion matrix: data/models/confusion_matrix.png

3. Evaluation

python evaluate.py

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🚀 Running the Application

Backend

cd src/api
python app.py

Server runs on http://localhost:8000

API Endpoints:

• GET / - Health check
• POST /analyze - Analyze single audio file
• POST /batch-analyze - Batch processing (up to 10 files)

Frontend

cd frontend
python -m http.server 3000

Open http://localhost:3000 in your browser

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💻 Usage

Web Interface

1. Click "ACTIVATE SHIELD"
2. Allow microphone access
3. Speak or play audio
4. Real-time results appear every 3 seconds

API Usage

import requests

# Upload audio file
with open('test_audio.wav', 'rb') as f:
    files = {'file': f}
    response = requests.post('http://localhost:8000/analyze', files=files)
    
result = response.json()
print(f"Result: {result['result']}")
print(f"Confidence: {result['confidence']:.1f}%")

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🔬 Technical Deep Dive

Why This Approach Works

1. Multi-Feature Analysis

Human speech and AI-generated speech differ in:

Feature	Real Speech	Fake Speech
Phase Continuity	Smooth transitions	Micro-breaks
Spectral Shape	Natural variations	Perfect but unnatural patterns
Silence Patterns	Natural pauses	Robotic gaps
Formant Structure	Complex harmonics	Simplified artifacts

2. MFCC Features

MFCCs capture the vocal tract shape - how sound is produced. AI models struggle to replicate the subtle imperfections of human vocal cords.

3. Attention Mechanism

Not all parts of audio are equally important. Attention helps the model focus on:

• Transition regions between phonemes
• Breath sounds
• Background artifacts

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📊 Performance

Metrics (ASVspoof 2019 LA Dataset)

Metric	Score
Accuracy	88.5%
Precision	89.2%
Recall	87.8%
F1-Score	88.5%
AUC-ROC	0.94

Inference Speed

• Average: 150ms per 3-second clip
• Hardware: CPU (Intel i7)
• Real-time: ✅ Yes (under 200ms threshold)

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🎨 Screenshots

Main Interface

Detection in Action

Explainability Heatmap

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🛣️ Roadmap

• Core detection model
• Real-time API
• Web interface
• Explainability (Grad-CAM)
• Mobile app (React Native)
• Browser extension
• Phone call integration
• Multi-language support
• Cloud deployment

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🤝 Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit changes (git commit -m 'Add amazing feature')
4. Push to branch (git push origin feature/amazing-feature)
5. Open a Pull Request

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⚖️ Legal & Ethics

Dataset Usage

This project uses the ASVspoof 2019 dataset for training. The dataset is used strictly for non-commercial research in compliance with its distribution license.

Trademarks

"ElevenLabs," "VALL-E," and other product names are trademarks of their respective owners. This project is not affiliated with these entities.

Privacy

Truth-Lens does not:

• Store audio recordings
• Transmit audio to external servers (when self-hosted)
• Record conversation content

Truth-Lens only analyzes:

• Audio signal integrity
• Spectral patterns
• Statistical features

Responsible Use

This tool should be used to:

• ✅ Verify authenticity of audio evidence
• ✅ Protect against voice-based fraud
• ✅ Educate about deepfake threats

This tool should NOT be used to:

• ❌ Violate privacy
• ❌ Harass individuals
• ❌ Enable illegal surveillance

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📄 License

This project is licensed under the MIT License - see LICENSE file for details.

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🙏 Acknowledgments

• ASVspoof Challenge for the benchmark dataset
• Librosa for audio processing
• TensorFlow team
• FastAPI framework

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📧 Contact

Project Lead: Your Name
Email: your.email@example.com
GitHub: @yourusername
LinkedIn: Your Profile

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🏆 Hackathon Information

Event: Quantumard National Hackathon 2026
Track: Artificial Intelligence & Machine Learning
Team: Truth-Lens Innovations

Problem Addressed: Audio deepfakes pose a growing threat to digital trust and security. Truth-Lens provides a real-time, explainable solution.

Innovation: First system to combine multi-feature ensemble learning with attention mechanisms and real-time explainability for audio deepfake detection.

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Made with ❤️ for a safer digital future