FakeLenseV2 🔍

An AI-Powered Fake News Detection System Integrating LLMs and Deep Reinforcement Learning

국가정보원(NIS) 주관 「2025 국가 방첩 우수논문」 수상작

국방부 주관 「2025 국방과 보안 우수논문」 수상작

🌟 Overview

Fake news and disinformation have become pervasive threats to societies, shaping public opinion, influencing political discourse, and eroding trust in credible information sources. The rapid evolution of misinformation tactics necessitates adaptive and robust detection mechanisms that go beyond traditional machine learning approaches.

FakeLenseV2 introduces an AI-powered fake news detection framework that integrates Natural Language Processing (NLP) and Deep Reinforcement Learning (DRL) to enhance classification accuracy, adaptability, and robustness. Unlike static classifiers, FakeLenseV2 iteratively refines its decision-making process, ensuring superior resilience against evolving misinformation strategies.

Evolution from FakeLenseV1

FakeLenseV1 was an LLM-driven fake news detection model that leveraged BERT for deep text comprehension and GPT for generative insights.

🔗 FakeLenseV1 GitHub Repository

Building on this foundation, FakeLenseV2 introduces:

✅ LLM-based embeddings for robust text representation
✅ Deep Q-Networks (DQN) with residual learning for dynamic classification strategies
✅ Adaptive reward mechanism to improve long-term learning efficiency
✅ Multi-modal feature integration (text + source credibility + social signals)

🚀 Key Features

1. Advanced NLP with Transformer Models

Leverages state-of-the-art LLMs (BERT, RoBERTa) for contextual embeddings
Captures semantic nuances and linguistic patterns in deceptive content
Surpasses traditional bag-of-words and TF-IDF methods

2. Deep Reinforcement Learning Framework

Deep Q-Network (DQN) with residual connections for improved stability
Double DQN (DDQN) to mitigate Q-value overestimation bias
Target network smoothing (τ = 0.005) for reduced training volatility
Adaptive reward shaping that incentivizes correct classifications

3. Multi-Modal Feature Integration

Source Credibility Scoring: Assigns reliability scores based on news source trustworthiness
Social Engagement Metrics: Incorporates shares, likes, and public reception data
Temporal Patterns: Analyzes propagation speed and viral characteristics

4. Production-Ready Architecture

GPU-accelerated for real-time detection
Batch inference support for large-scale monitoring
FastAPI REST API with automatic documentation
- Confidence scoring for all predictions
- Structured JSON logging with request tracking
- Rate limiting (100 req/min per IP)
- CORS support for cross-origin requests
Docker support for easy deployment
CI/CD pipeline with automated testing
Integration-ready for social media platforms and fact-checking systems

📁 Project Structure

FakeLenseV2/
├── code/
│   ├── models/              # Neural network architectures
│   │   ├── dqn.py          # DQN and DQNResidual models
│   │   └── vectorizer.py   # BERT/RoBERTa text embedding
│   ├── agents/              # Reinforcement learning agents
│   │   └── fake_news_agent.py
│   ├── utils/               # Utility modules
│   │   ├── config.py       # Configuration management
│   │   ├── feature_extraction.py
│   │   ├── validators.py   # Data validation
│   │   └── exceptions.py   # Custom exceptions
│   ├── train.py             # Training pipeline
│   ├── inference.py         # Inference engine
│   ├── main.py              # CLI interface
│   └── api_server.py        # FastAPI REST API
├── tests/                   # Unit tests
├── data/                    # Training and test data
├── models/                  # Saved model checkpoints
├── Dockerfile               # Docker configuration
├── docker-compose.yml       # Docker Compose setup
├── requirements.txt         # Python dependencies
├── setup.py                 # Package installation
└── README_USAGE.md         # Detailed usage guide

🏗️ System Architecture

┌─────────────────────────────────────────────────────────────┐
│                     Input Layer                             │
│  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐       │
│  │ News Article │  │ Source Info  │  │ Social Data  │       │
│  └──────┬───────┘  └──────┬───────┘  └──────┬───────┘       │
└─────────┼─────────────────┼─────────────────┼───────────────┘
          │                 │                 │
          ▼                 ▼                 ▼
┌─────────────────────────────────────────────────────────────┐
│                  Feature Extraction                         │
│  ┌──────────────────────────────────────────────────────┐   │
│  │  BERT/RoBERTa Embeddings (768-dim vectors)           │   │
│  └──────────────────────────────────────────────────────┘   │
│  ┌──────────────────────────────────────────────────────┐   │
│  │  Source Credibility Encoding (trust scores)          │   │
│  └──────────────────────────────────────────────────────┘   │
│  ┌──────────────────────────────────────────────────────┐   │
│  │  Social Reaction Normalization (engagement metrics)  │   │
│  └──────────────────────────────────────────────────────┘   │
└─────────┬───────────────────────────────────────────────────┘
          │
          ▼
┌─────────────────────────────────────────────────────────────┐
│              Deep Q-Network (DQN) Agent                     │
│  ┌──────────────────────────────────────────────────────┐   │
│  │  State: [Text Embedding + Meta Features]             │   │
│  └──────────────────────────────────────────────────────┘   │
│  ┌──────────────────────────────────────────────────────┐   │
│  │  Q-Network: 3-Layer ResNet with Layer Normalization  │   │
│  │  - Hidden Layer 1: 512 units + Residual              │   │
│  │  - Hidden Layer 2: 256 units + Residual              │   │
│  │  - Output Layer: 3 actions (Real/Suspicious/Fake)    │   │
│  └──────────────────────────────────────────────────────┘   │
│  ┌──────────────────────────────────────────────────────┐   │
│  │  Target Network: Soft updates (τ = 0.005)            │   │
│  └──────────────────────────────────────────────────────┘   │
└─────────┬───────────────────────────────────────────────────┘
          │
          ▼
┌─────────────────────────────────────────────────────────────┐
│                    Output Layer                             │
│        Classification: {0: Fake, 1: Suspicious, 2: Real}    │
│        Confidence Score: [0.0 - 1.0]                        │
└─────────────────────────────────────────────────────────────┘

🔧 Technical Components

1. Feature Engineering Module

Text Tokenization & Embedding

- Transformer: BERT-base-uncased / RoBERTa-base
- Embedding Dimension: 768
- Pooling Strategy: [CLS] token or mean pooling
- Maximum Sequence Length: 512 tokens

Source Credibility Encoding

- Credibility Database: Media bias ratings (MBFC, Ad Fontes)
- Score Range: [0.0, 1.0]
- High-trust sources: Reuters, BBC, AP News (0.9-1.0)
- Low-trust sources: Sensationalist outlets (0.0-0.3)

Social Reaction Normalization

- Metrics: Shares, likes, comments, retweets
- Normalization: Min-Max scaling
- Feature: Social engagement velocity

2. Reinforcement Learning Agent

State Space

State = [Text_Embedding (768-dim), Source_Score (1-dim), Social_Score (1-dim)]
Total Dimension: 770

Action Space

Actions = {
    0: Classify as "Fake News",
    1: Classify as "Suspicious",
    2: Classify as "Real News"
}

Reward Function

Reward(s, a, s') = {
    +1.0    if correct classification
    -0.5    if incorrect with low confidence
    -1.0    if incorrect with high confidence (overconfidence penalty)
    +0.2    bonus for correct "Suspicious" on ambiguous cases
}

DQN Architecture

class ResidualDQN(nn.Module):
    Input Layer: 770 → 512 (ReLU + LayerNorm)
    Residual Block 1: 512 → 512 + skip connection
    Residual Block 2: 512 → 256 + skip connection
    Output Layer: 256 → 3 (Q-values for each action)

3. Training Strategy

Experience Replay Buffer: 10,000 transitions
Batch Size: 64
Learning Rate: 1e-4 (Adam optimizer)
Discount Factor (γ): 0.99
Exploration (ε-greedy): ε starts at 1.0, decays to 0.01
Target Network Update: Soft update with τ = 0.005
Early Stopping: Patience = 15 episodes

📦 Installation

Prerequisites

Python 3.8 or higher
CUDA 11.0+ (for GPU acceleration)
8GB+ RAM recommended

Step 1: Clone the Repository

git clone https://github.com/Navy10021/FakeLenseV2.git
cd FakeLenseV2

Step 2: Create Virtual Environment (Recommended)

# Using venv
python -m venv fakelense_env
source fakelense_env/bin/activate  # On Windows: fakelense_env\Scripts\activate

# Or using conda
conda create -n fakelense python=3.8
conda activate fakelense

Step 3: Install Dependencies

# Install dependencies
pip install -r requirements.txt

# Or install as a package (recommended)
pip install -e .

Core Dependencies:

torch>=1.12.0
transformers>=4.20.0
numpy>=1.21.0
pandas>=1.3.0
scikit-learn>=1.0.0
matplotlib>=3.4.0
seaborn>=0.11.0
tqdm>=4.62.0
fastapi>=0.100.0
uvicorn>=0.23.0
pydantic>=2.0.0
slowapi>=0.1.9  # For rate limiting

Alternative: Docker Installation

# Using Docker Compose (recommended)
docker-compose up fakelense-api

# Or build manually
docker build -t fakelensev2:latest .
docker run -p 8000:8000 fakelensev2:latest

💻 Usage

📖 For detailed usage examples, see README_USAGE.md

Quick Start

1. Command Line Interface

# Training
python -m code.main train --config config.example.json

# Evaluation
python -m code.main evaluate --model models/best_model.pth

# Single Prediction
python -m code.main infer \
    --model models/best_model.pth \
    --text "Scientists discover new planet..." \
    --source "Reuters" \
    --reactions 5000

2. Python API

from code.inference import InferenceEngine

# Initialize engine (loads model once)
engine = InferenceEngine("models/best_model.pth")

# Make prediction
prediction = engine.predict(
    text="Scientists at NASA announce discovery...",
    source="Reuters",
    social_reactions=5000
)

# 0=Fake, 1=Suspicious, 2=Real
print(f"Prediction: {prediction}")

3. REST API Server

# Start server
python -m code.api_server

# Or using Docker
docker-compose up fakelense-api

Make API requests:

curl -X POST http://localhost:8000/predict \
  -H "Content-Type: application/json" \
  -d '{
    "text": "Breaking news article...",
    "source": "Reuters",
    "social_reactions": 5000
  }'

Response:

{
  "prediction": 2,
  "label": "Real News",
  "confidence": 0.95,
  "all_probabilities": {
    "fake": 0.02,
    "suspicious": 0.03,
    "real": 0.95
  },
  "request_id": "a1b2c3d4-e5f6-7890-abcd-ef1234567890"
}

API Documentation:

Swagger UI: http://localhost:8000/docs
Detailed Examples: See API_EXAMPLES.md

4. Docker Deployment

# Start API server
docker-compose up fakelense-api

# Run training (one-time)
docker-compose run --rm fakelense-train

# View logs
docker-compose logs -f fakelense-api

📊 Performance

Experimental Results

FakeLenseV2 achieved 97.2% accuracy on the benchmark dataset, demonstrating state-of-the-art performance in fake news detection.

Metric	Score
Accuracy	97.2%
Precision	96.8%
Recall	97.5%
F1-Score	97.1%

Confusion Matrix

                Predicted
              Fake  Sus  Real
Actual Fake   [485   12    3]
       Sus    [ 8   290   15]
       Real   [ 2    11  487]

Ablation Study

Performance impact of key components:

Configuration	Accuracy
Full Model (FakeLenseV2)	97.2%
Without Reinforcement Learning	93.5%
Without Source Credibility	94.8%
Without Social Metrics	95.2%
Without BERT Embeddings	89.1%
Baseline (Traditional ML)	85.3%

Comparison with Other Methods

Model	Accuracy	F1-Score
FakeLenseV2 (Ours)	97.2%	97.1%
FakeLenseV1	95.8%	95.4%
BERT-Only Classifier	94.2%	93.8%
LSTM + Attention	91.5%	90.9%
Random Forest	87.3%	86.5%
Logistic Regression	82.1%	81.3%

📚 Datasets

FakeLenseV2 has been trained and evaluated on multiple benchmark datasets:

Primary Datasets

LIAR Dataset (Wang, 2017)
- 12,836 labeled statements
- 6 fine-grained labels for truthfulness
FakeNewsNet (Shu et al., 2018)
- PolitiFact: 314 news articles
- GossipCop: 5,464 news articles
- Includes social context features
ISOT Fake News Dataset
- 44,898 articles (21,417 real + 23,481 fake)
- Collected from Reuters and unreliable sources

Data Preprocessing

# Example preprocessing pipeline
from preprocessing import prepare_dataset

train_data, test_data = prepare_dataset(
    dataset_path='./data/raw/',
    train_split=0.8,
    max_length=512,
    include_metadata=True
)

🗺️ Roadmap

Current Status (v2.0)

✅ Deep Q-Learning integration
✅ Multi-modal feature fusion
✅ Source credibility scoring
✅ Social engagement analysis

Upcoming Features (v2.1)

🔄 Multi-language support (Korean, Spanish, French)
🔄 Explainable AI (XAI) module with attention visualization
🔄 Active learning for continuous model improvement
🔄 Graph Neural Networks for propagation pattern analysis

Future Directions (v3.0)

📌 Adversarial robustness testing and defense mechanisms
📌 Cross-platform integration (Twitter, Facebook, YouTube APIs)
📌 Real-time monitoring dashboard
📌 Federated learning for privacy-preserving training

📚 Documentation

README_USAGE.md - Detailed usage guide with examples
IMPROVEMENTS.md - Complete changelog and improvements
CONTRIBUTING.md - Contribution guidelines
API Documentation - Interactive API docs (when server is running)

🤝 Contributing

We welcome contributions from the community!

📖 See CONTRIBUTING.md for detailed guidelines

Quick Start for Contributors

# 1. Fork and clone
git clone https://github.com/YOUR_USERNAME/FakeLenseV2.git
cd FakeLenseV2

# 2. Install development dependencies
pip install -e ".[dev]"

# 3. Run tests
pytest tests/ -v

# 4. Create a branch and make changes
git checkout -b feature/your-feature-name

# 5. Submit pull request

Ways to Contribute

🐛 Report bugs via Issues
💡 Suggest features
📖 Improve documentation
🔧 Submit pull requests
⭐ Star the repository!

📄 License

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

MIT License

Copyright (c) 2024 Navy Lee, Seoul National University

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction...

🙏 Acknowledgments

Research Team

Navy Lee - Principal Investigator
Seoul National University Graduate School of Data Science (SNU GSDS)

Special Thanks

Hugging Face team for the Transformers library
PyTorch team for the deep learning framework
The open-source community for valuable feedback

Citations

If you use FakeLenseV2 in your research, please cite:

@software{fakelensev2_2024,
  author = {Lee, Navy},
  title = {FakeLenseV2: An AI-Powered Fake News Detection System Integrating LLMs and Deep Reinforcement Learning},
  year = {2024},
  publisher = {GitHub},
  url = {https://github.com/Navy10021/FakeLenseV2}
}

📞 Contact

For questions, suggestions, or collaborations:

GitHub Issues: Create an issue
Email: [iyunseob4@gmail.com]

🌟 Star this repository if you find it helpful!

Made with ❤️ by the SNU GSDS Research Team

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code		code
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models		models
notebook		notebook
tests		tests
.gitignore		.gitignore
API_EXAMPLES.md		API_EXAMPLES.md
CONTRIBUTING.md		CONTRIBUTING.md
Dockerfile		Dockerfile
IMPROVEMENTS.md		IMPROVEMENTS.md
IMPROVEMENT_RECOMMENDATIONS.md		IMPROVEMENT_RECOMMENDATIONS.md
LICENSE		LICENSE
PR_DESCRIPTION.md		PR_DESCRIPTION.md
README.md		README.md
README_USAGE.md		README_USAGE.md
config.example.json		config.example.json
docker-compose.yml		docker-compose.yml
requirements-optional.txt		requirements-optional.txt
requirements.txt		requirements.txt
setup.py		setup.py

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