🎯 Discord Amazon Rekognition

A Discord bot that integrates with AWS Rekognition to provide image analysis, face detection, text extraction, celebrity recognition, content moderation, and face comparison directly within Discord servers.

This bot can be used in OSINT workflows that involve image analysis, such as object identification, text extraction, and face comparison.

📸 Preview

Face comparison: ✅

Face comparison: ❌

Analysis results: celebrities

Analysis results: text

📝 Table of Contents

Click to expand

✨ Features
🚀 Commands
📋 Prerequisites
🛠️ Installation
🔑 AWS Setup
🔧 Discord Setup
⚡ Running the Bot
🔗 Bot Invitation
📁 Project Structure
🎯 Usage Examples
📈 Performance
🐛 Troubleshooting
🔧 Development
📊 API Limits
🛡️ Security
🤝 Contributing

✨ Features

🔍 Comprehensive Image Analysis

Object & Scene Detection: Identify thousands of objects, scenes, and concepts
Text Extraction (OCR): Extract text from images with high accuracy
Face Analysis: Detect faces with demographics, emotions, and facial attributes
Celebrity Recognition: Identify famous people in images
Content Moderation: Automatically detect inappropriate content
Face Comparison: Compare faces between two images with similarity scoring

🎯 Advanced Capabilities

Batch Processing: Analyze multiple features simultaneously
AWS-Powered: Uses AWS Rekognition's machine learning models
Detailed Reports: Export comprehensive JSON analysis reports
Image Support: Works with URLs and uploaded images (JPEG, PNG)
Real-time Processing: Fast analysis with progress indicators
Error Handling: Robust error management with user-friendly messages

💡 User Experience

Slash Commands: Modern Discord interface with autocomplete
Visual Results: Rich embeds with thumbnails and organized data
File Attachments: Detailed JSON reports for further analysis
Progress Updates: Real-time status updates during processing
Smart Validation: Input validation and helpful error messages

🚀 Commands

`/rekognition analyze`

Perform comprehensive image analysis with multiple AI features.

Options:

url (optional): Direct image URL to analyze
image (optional): Upload an image file to analyze
features (optional): Specific features to analyze

Available Features:

All Features (Recommended): Run complete analysis
Labels & Objects: Detect objects, scenes, and concepts
Text Detection (OCR): Extract text from images
Face Analysis: Analyze faces, emotions, and demographics
Content Moderation: Check for inappropriate content
Celebrity Recognition: Identify famous people

Example:

/rekognition analyze image:[upload] features:All Features
/rekognition analyze url:https://example.com/image.jpg features:Face Analysis

`/rekognition compare`

Compare faces between two images with similarity scoring.

Options:

source_url / source_image: Reference image with the face to match
target_url / target_image: Image to search for matching faces
similarity (optional): Minimum similarity threshold (0-100, default: 80)

Example:

/rekognition compare source_image:[upload1] target_image:[upload2] similarity:75
/rekognition compare source_url:https://example.com/face1.jpg target_url:https://example.com/face2.jpg

📋 Prerequisites

Node.js v18.0.0 or higher (or Bun v1.0+)
Docker (optional, recommended for production)
Discord Application and bot token
AWS Account with Rekognition access
AWS IAM User with appropriate permissions

🛠️ Installation

1. Clone the Repository

git clone https://github.com/gl0bal01/discord-amazon-rekognition.git
cd discord-amazon-rekognition

2. Install Dependencies

bun install

3. Set Up Environment Variables

cp .env.example .env

Edit .env with your configuration:

# Discord Configuration
DISCORD_TOKEN=your_discord_bot_token_here
CLIENT_ID=your_discord_application_client_id_here
GUILD_ID=your_development_server_id_here

# AWS Configuration
AWS_ACCESS_KEY_ID=your_aws_access_key_id_here
AWS_SECRET_ACCESS_KEY=your_aws_secret_access_key_here
AWS_REGION=us-east-1

🔑 AWS Setup

1. Create AWS Account

Sign up at aws.amazon.com if you don't have an account.

2. Create IAM User

Go to IAM Console → Users → Create User
Choose "Programmatic access"
Create or attach a policy with these permissions:

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "rekognition:DetectLabels",
                "rekognition:DetectText",
                "rekognition:DetectFaces",
                "rekognition:DetectModerationLabels",
                "rekognition:RecognizeCelebrities",
                "rekognition:CompareFaces"
            ],
            "Resource": "*"
        }
    ]
}

3. Get Credentials

Copy the Access Key ID and Secret Access Key
Add them to your .env file

4. Choose AWS Region

Select the region closest to your users:

us-east-1 (N. Virginia) - Default
us-west-2 (Oregon)
eu-west-1 (Ireland)
ap-southeast-1 (Singapore)

🔧 Discord Setup

1. Create Discord Application

Go to Discord Developer Portal
Click "New Application" and name your bot
Go to "Bot" section and create a bot
Copy the Bot Token to your .env file

2. Get Client ID

In the "General Information" section
Copy the Application ID (this is your Client ID)

3. Bot Permissions

Your bot needs these permissions:

Send Messages (2048)
Use Slash Commands (2147483648)
Attach Files (32768)
Embed Links (16384)

Permission Integer: 2147516160

⚡ Running the Bot

1. Deploy Commands

# Development (instant deployment to test server)
bun run deploy

# Production (global deployment - takes up to 1 hour)
bun run deploy:global

2. Start the Bot

# Production
bun run start

# Development with auto-restart
bun run dev

3. Docker (Recommended for Production)

# Build
docker build -t rekognition-bot .

# Run
docker run -d \
  --name rekognition-bot \
  --read-only \
  --tmpfs /app/temp:rw,noexec,nosuid,size=100m \
  --memory=512m \
  --cpus=1.0 \
  --pids-limit=50 \
  --security-opt=no-new-privileges:true \
  --env-file .env \
  --restart unless-stopped \
  rekognition-bot

4. Verify Startup

Look for these startup messages:

✅ Loaded command: rekognition
📁 Created temporary files directory
🎯 Discord Amazon Rekognition Bot is ready!
📊 Logged in as: YourBotName#1234

🔗 Bot Invitation

Use this URL to invite your bot (replace YOUR_CLIENT_ID):

https://discord.com/api/oauth2/authorize?client_id=YOUR_CLIENT_ID&permissions=2147516160&scope=bot%20applications.commands

Required Permissions:

Send Messages: Respond to commands
Use Slash Commands: Register slash commands
Attach Files: Send analysis reports and images
Embed Links: Display rich result embeds

📁 Project Structure

discord-amazon-rekognition/
├── commands/
│   └── rekognition.js     # Main Rekognition command
├── tests/
│   └── rekognition.test.js # Unit tests (bun test)
├── temp/                  # Temporary file storage (auto-created)
├── .dockerignore         # Docker build exclusions
├── .env.example          # Environment variables template
├── .gitignore            # Git ignore rules
├── deploy-commands.js    # Command deployment script
├── Dockerfile            # Multi-stage production container
├── index.js              # Main bot application
├── LICENSE               # MIT license
├── package.json          # Dependencies and scripts
└── README.md             # This documentation

🎯 Usage Examples

Example 1: Comprehensive Image Analysis

/rekognition analyze
📎 Upload: family_photo.jpg
🎯 Features: All Features

Results:

Objects: Person (99.5%), Clothing (95.2%), Smile (87.1%)
Text: "Happy Birthday!" extracted from banner
Faces: 3 faces detected with age ranges and emotions
JSON Report: Detailed technical analysis attached

Example 2: Face Comparison

/rekognition compare
📎 Source: person1.jpg
📎 Target: group_photo.jpg
📊 Similarity: 75%

Results:

Match Found: 89.2% similarity detected
Additional Faces: 4 unmatched faces in target image
Comparison Report: Detailed matching data

Example 3: Text Extraction (OCR)

/rekognition analyze
🔗 URL: https://example.com/document.jpg
🎯 Features: Text Detection (OCR)

Results:

Extracted Text: All readable text from the image
Confidence Scores: Accuracy ratings for each text element
Bounding Boxes: Location data in JSON report

📈 Performance

Metric	Value
Analysis Speed	Varies by image size, network latency, and AWS region
Supported Formats	JPEG, PNG
Max Image Size	5MB (JPEG), 8MB (PNG)
Accuracy	Depends on feature and image quality; see AWS Rekognition documentation for details
Concurrent Requests	10 (configurable)

🐛 Troubleshooting

Common Issues

❌ "AWS credentials are not configured"

Solution:

Verify AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY are in .env
Check that credentials are valid
Ensure IAM user has Rekognition permissions

❌ "Invalid image format"

Solution:

Use JPEG or PNG format only
Ensure image file is not corrupted
Check image size limits (5MB JPEG, 8MB PNG)

❌ "No faces detected"

Solution:

Ensure faces are clearly visible and well-lit
Use higher resolution images
Avoid heavily obscured or profile faces

❌ Commands not appearing

Solution:

Run bun run deploy to update commands
Check CLIENT_ID is correct in .env
Wait up to 1 hour for global command deployment

Debug Mode

Enable detailed logging:

NODE_ENV=development

AWS Pricing

Monitor your usage at AWS Billing Console:

Free Tier: 5,000 images/month for first 12 months
Standard Pricing: $1-5 per 1,000 images (varies by feature)

📊 API Limits

AWS Rekognition Limits

Rate Limits: 50 TPS (transactions per second)
Image Size: 5MB JPEG, 8MB PNG
Image Resolution: Minimum 80 pixels for face detection
Concurrent Requests: 50 per account

Discord Limits

Message Size: 2000 characters
File Upload: 25MB (Discord Nitro: 100MB)
Embeds: 25 fields per embed

🛡️ Security

Data Privacy

No Image Storage: Images are processed and immediately deleted
Temporary Files: Auto-cleanup after 10 seconds per request
AWS Security: All data encrypted in transit and at rest
No Logging: Personal image data is never logged

Security Hardening

SSRF Protection: DNS resolution + private IP blocking (IPv4, IPv6, IPv4-mapped IPv6, 6to4, Teredo) with pinned HTTP agents to prevent DNS rebinding
Path Traversal Prevention: Random filenames with extension allowlisting
Magic Bytes Validation: File signatures verified (JPEG, PNG, GIF, BMP, WebP)
Rate Limiting: Per-user cooldown + global concurrency limit
Error Sanitization: Internal errors never exposed to users
Docker: Runs as non-root, read-only filesystem, tini init, pinned base image

Best Practices

Use IAM roles (preferred) or static credentials with minimal permissions
Deploy with Docker using the recommended docker run flags
Regularly rotate access keys if using static credentials
Monitor AWS CloudTrail logs
Keep dependencies updated (bun update)

Content Moderation

The bot includes automatic content moderation features to detect:

Explicit content
Suggestive content
Violence
Visually disturbing content

🔧 Development

Adding New Features

Fork the repository
Create a feature branch
Implement your changes
Add appropriate error handling
Update documentation
Submit a pull request

AWS SDK v3 Usage

This bot uses AWS SDK v3 for better performance:

const { RekognitionClient, DetectLabelsCommand } = require('@aws-sdk/client-rekognition');

const client = new RekognitionClient({ region: 'us-east-1' });
const command = new DetectLabelsCommand(params);
const response = await client.send(command);

Testing

bun test

110 unit tests covering SSRF protection, magic bytes validation, URL validation, extension sanitization, and IPv6 edge cases.

🔗 Quick Links

Resource	Link
📚 AWS Rekognition Docs	AWS Documentation
🐛 Report Bug	GitHub Issues
✨ Request Feature	Feature Request
💬 Discord Support	Join Server
🔧 AWS Console	AWS Management Console

📄 License

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

🤝 Contributing

Fork the repository
Create a feature branch (git checkout -b feature/amazing-feature)
Commit your changes (git commit -m 'Add some amazing feature')
Push to the branch (git push origin feature/amazing-feature)
Open a Pull Request

🆘 Support

Create an issue for bug reports
Join our Discord server for support
Check AWS Rekognition documentation for API questions
Review Discord.js guide for bot development help

🙏 Acknowledgments

AWS Rekognition - Cloud-based computer vision service
Discord.js - Excellent Discord API library
Discord Developer Portal - Bot development platform
AWS SDK for JavaScript - Official AWS SDK

Made for image analysis workflows

⭐ Star this repo if you find it helpful!

Want to contribute? We welcome PRs, bug reports, and feature requests!

Name		Name	Last commit message	Last commit date
Latest commit History 14 Commits
.github/workflows		.github/workflows
assets		assets
commands		commands
tests		tests
.dockerignore		.dockerignore
.env.example		.env.example
.gitignore		.gitignore
CITATION.cff		CITATION.cff
Dockerfile		Dockerfile
LICENSE		LICENSE
README.md		README.md
bun.lock		bun.lock
deploy-commands.js		deploy-commands.js
index.js		index.js
package.json		package.json

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🎯 Discord Amazon Rekognition

📸 Preview

📝 Table of Contents

✨ Features

🔍 Comprehensive Image Analysis

🎯 Advanced Capabilities

💡 User Experience

🚀 Commands

/rekognition analyze

/rekognition compare

📋 Prerequisites

🛠️ Installation

1. Clone the Repository

2. Install Dependencies

3. Set Up Environment Variables

🔑 AWS Setup

1. Create AWS Account

2. Create IAM User

3. Get Credentials

4. Choose AWS Region

🔧 Discord Setup

1. Create Discord Application

2. Get Client ID

3. Bot Permissions

⚡ Running the Bot

1. Deploy Commands

2. Start the Bot

3. Docker (Recommended for Production)

4. Verify Startup

🔗 Bot Invitation

📁 Project Structure

🎯 Usage Examples

Example 1: Comprehensive Image Analysis

Example 2: Face Comparison

Example 3: Text Extraction (OCR)

📈 Performance

🐛 Troubleshooting

Common Issues

❌ "AWS credentials are not configured"

❌ "Invalid image format"

❌ "No faces detected"

❌ Commands not appearing

Debug Mode

AWS Pricing

📊 API Limits

AWS Rekognition Limits

Discord Limits

🛡️ Security

Data Privacy

Security Hardening

Best Practices

Content Moderation

🔧 Development

Adding New Features

AWS SDK v3 Usage

Testing

🔗 Quick Links

📄 License

🤝 Contributing

🆘 Support

🙏 Acknowledgments

⭐ Star this repo if you find it helpful!

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