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https://github.com/exios66/ai-deception-framework
https://github.com/exios66/ai-deception-framework
Last synced: 10 days ago
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- Host: GitHub
- URL: https://github.com/exios66/ai-deception-framework
- Owner: Exios66
- Created: 2024-10-22T00:48:19.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2024-10-23T01:35:46.000Z (3 months ago)
- Last Synced: 2024-10-23T05:48:20.214Z (3 months ago)
- Language: JavaScript
- Homepage: https://exios66.github.io/Ai-Deception-Framework/
- Size: 269 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
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README
# AI Deception Framework
A comprehensive framework for analyzing and detecting deception in AI systems, with integration to the Literary Vault API.
## Features
- AI Model Analysis
- Deception Detection
- Question Analysis Integration
- Real-time Metrics Dashboard
- Literary Vault API Integration
- Open Source Collaboration
## Installation
1. Clone the repository:
```bash
git clone https://github.com/Exios66/Ai-Deception-Framework.git
cd Ai-Deception-Framework
```
2. Install dependencies:
```bash
pip install -r requirements.txt
```
3. Set up environment variables:
```bash
cp .env.example .env
# Edit .env with your configuration:
# GITHUB_TOKEN=your_github_token
# DATABASE_URL=sqlite:///questions.db
# JWT_SECRET=your_secret_key
# CORS_ORIGINS=http://localhost:3000,https://yourdomain.com
# API_KEY=your_openai_api_key
```
## API Endpoints
### 1. Model Analysis
Analyze AI models for potential deception:
```bash
# Upload and analyze a model
curl -X POST "http://localhost:8000/api/v1/analysis/model" \
-H "Content-Type: multipart/form-data" \
-F "file=@/path/to/model.h5"
```
Response:
```json
{
"model_type": "neural_network",
"accuracy": 0.918,
"deception_points": [
"Potential bias in output layer",
"Unusual activation patterns"
],
"recommendation": "Consider reviewing the model's training data for potential biases",
"confidence_score": 0.85
}
```
### 2. Deception Detection
Analyze content for potential deception:
```bash
# Analyze text content
curl -X POST "http://localhost:8000/api/v1/detection/analyze" \
-H "Content-Type: application/json" \
-d '{
"content": "Your text content here",
"context": "Optional context"
}'
```
Response:
```json
{
"probability": 0.342,
"confidence": 0.89,
"issues": [
"Inconsistent narrative",
"Unusual language patterns"
],
"recommendations": [
"Review content for consistency",
"Verify source authenticity"
]
}
```
### 3. Literary Vault Integration
#### Get Questions
```bash
# Get questions from a specific category
curl "http://localhost:8000/api/v1/literary-vault/questions/astronomy?limit=5&random=true"
```
Response:
```json
[
{
"id": "q123",
"question": "What is the closest star to Earth?",
"correct_answer": "The Sun",
"options": ["The Sun", "Proxima Centauri", "Alpha Centauri", "Sirius"]
}
]
```
#### Randomize Questions
```bash
# Get randomized questions
curl -X POST "http://localhost:8000/api/v1/literary-vault/questions/randomize" \
-H "Content-Type: application/json" \
-d '{
"category": "astronomy",
"count": 5,
"seed": 12345
}'
```
### 4. Question Analysis
Analyze questions for potential deception:
```bash
# Analyze a set of questions
curl -X POST "http://localhost:8000/api/v1/detection/analyze-questions" \
-H "Content-Type: application/json" \
-d '[{
"id": "q123",
"question": "What is the closest star to Earth?",
"correct_answer": "The Sun"
}]'
```
## Python Client Examples
```python
import httpx
import asyncio
async def analyze_model(file_path: str):
async with httpx.AsyncClient() as client:
files = {'file': open(file_path, 'rb')}
response = await client.post(
'http://localhost:8000/api/v1/analysis/model',
files=files
)
return response.json()
async def detect_deception(content: str):
async with httpx.AsyncClient() as client:
response = await client.post(
'http://localhost:8000/api/v1/detection/analyze',
json={'content': content}
)
return response.json()
async def get_and_analyze_questions():
async with httpx.AsyncClient() as client:
# Get questions
questions = await client.get(
'http://localhost:8000/api/v1/literary-vault/questions/astronomy',
params={'limit': 5}
)
# Analyze them for deception
analysis = await client.post(
'http://localhost:8000/api/v1/detection/analyze-questions',
json=questions.json()
)
return analysis.json()
```
## Development
### Running Tests
```bash
pytest tests/
```
### Local Development
```bash
uvicorn api.main:app --reload
```
### Docker Deployment
```bash
docker build -t ai-deception-framework .
docker run -p 8000:8000 ai-deception-framework
```
## Contributing
1. Fork the repository
2. Create your feature branch (`git checkout -b feature/amazing-feature`)
3. Commit your changes (`git commit -m 'Add amazing feature'`)
4. Push to the branch (`git push origin feature/amazing-feature`)
5. Open a Pull Request
## API Documentation
Full API documentation is available at:
- Swagger UI: `http://localhost:8000/docs`
- ReDoc: `http://localhost:8000/redoc`
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## Acknowledgments
- Literary Vault API Integration
- OpenAI API Integration
- Contributors and maintainers
## Support
For support, please open an issue in the GitHub repository or contact the maintainers.