johnalbertini14-glitch NSFW Detector Pro
Metadata
- ID: nsfw-detector-pro
- Version: 1.0.0
- Category: content-filtering
- Priority: Critical
- Installation: ClawHub
- Package:
@raghulpasupathi/nsfw-detector-pro
Description
Advanced NSFW (Not Safe For Work) content detection system using computer vision, deep learning models, and multi-modal analysis. Detects explicit content in images, videos, and text with high accuracy and configurable sensitivity levels.
Installation
Via ClawHub
https://clawhub.ai/raghulpasupathi/nsfw-detector-pro
Via npm
npm install @raghulpasupathi/nsfw-detector-pro
Features
- Image Analysis: Detect nudity, sexual content, and explicit imagery
- Video Analysis: Frame-by-frame and scene-level detection
- Text Analysis: Identify sexual language and explicit descriptions
- Multi-class Detection: Pornography, erotica, suggestive, safe categories
- Confidence Scoring: 0-100% confidence for each classification
- Custom Thresholds: Adjustable sensitivity per use case
- Skin Tone Detection: Identify exposed skin regions accurately
- Context Awareness: Distinguish artistic from explicit content
- Real-time Processing: Fast inference for live content
- Batch Processing: Efficient analysis of multiple items
- Age Estimation: Detect potential underage content (refer to CSAM Shield)
- Model Ensemble: Combines multiple models for better accuracy
Configuration
{
"enabled": true,
"settings": {
"defaultSensitivity": "moderate",
"sensitivities": {
"strict": {
"pornography": 0.15,
"erotica": 0.25,
"suggestive": 0.40,
"blockThreshold": 0.15
},
"moderate": {
"pornography": 0.40,
"erotica": 0.60,
"suggestive": 0.75,
"blockThreshold": 0.40
},
"relaxed": {
"pornography": 0.70,
"erotica": 0.85,
"suggestive": 0.90,
"blockThreshold": 0.70
}
},
"models": {
"image": {
"enabled": true,
"model": "nsfw-resnet-50",
"useGPU": true
},
"video": {
"enabled": true,
"fps": 2,
"maxFrames": 30
},
"text": {
"enabled": true,
"model": "bert-nsfw-classifier"
}
},
"processing": {
"imageMaxSize": "4096x4096",
"videoMaxDuration": 300,
"batchSize": 32,
"cacheResults": true,
"cacheTTL": 86400
},
"advanced": {
"skinDetection": true,
"faceDetection": true,
"contextAnalysis": true,
"artFilter": true,
"medicalFilter": true
}
}
}
API Examples
See nsfw-detector-pro-examples.js for complete usage examples.
Dependencies
- @tensorflow/tfjs-node-gpu: ^4.0.0 - TensorFlow for GPU inference
- sharp: ^0.32.0 - Image processing
- opencv4nodejs: ^6.0.0 - Computer vision operations
- ffmpeg-fluent: ^2.1.0 - Video processing
- nsfw.js: ^3.0.0 - Pre-trained NSFW model
Performance
- Image Analysis: 50-800ms depending on image size
- Video Analysis: 3-6s for 30s video at 2fps
- Text Analysis: 20-200ms depending on text length
- Accuracy: 96% for pornography detection
- False Positive Rate: 2-4%
- False Negative Rate: 1-3%
Use Cases
- Social media content moderation
- Dating app photo verification
- User-generated content platforms
- Comment section filtering
- Profile picture screening
- Video streaming platforms
- E-commerce listings
- Community forums
- Educational platforms
- Corporate content filters
Best Practices
- Use appropriate sensitivity level for your use case
- Enable caching to reduce repeated analysis costs
- Use GPU acceleration for production workloads
- Implement appeal process for false positives
- Log all violations for audit trail
- Provide clear feedback to users on violations
- Enable art/medical filters if applicable
- Use batch processing for efficiency
- Monitor false positive/negative rates
- Regular model updates for improved accuracy
- Implement graceful degradation if service unavailable
- Consider user reputation/history in moderation decisions