> ## Documentation Index
> Fetch the complete documentation index at: https://docs.pixelpatrol.net/llms.txt
> Use this file to discover all available pages before exploring further.

# AI Moderation

> Advanced AI-powered content analysis and moderation

## Overview

Pixel Patrol's AI moderation uses state-of-the-art machine learning models to analyze images, videos, and text for potentially harmful content. Our AI provides detailed analysis with confidence scores, enabling accurate and nuanced moderation decisions.

## AI Capabilities

### Content Analysis

<CardGroup cols={2}>
  <Card title="Visual Analysis" icon="eye">
    * Object detection - Scene understanding - Text extraction (OCR) - Face
      detection - Brand/logo recognition
  </Card>

  <Card title="Context Understanding" icon="lightbulb">
    * Semantic analysis - Context awareness - Cultural sensitivity - Sarcasm
      detection - Intent classification
  </Card>
</CardGroup>

### Detection Categories

Our AI models detect multiple content categories:

| Category       | Description                          | Confidence Range |
| -------------- | ------------------------------------ | ---------------- |
| Violence       | Graphic violence, weapons, gore      | 0.0 - 1.0        |
| Adult          | Nudity, sexual content               | 0.0 - 1.0        |
| Hate           | Discriminatory content, hate symbols | 0.0 - 1.0        |
| Self-Harm      | Content promoting self-injury        | 0.0 - 1.0        |
| Drugs          | Drug use, paraphernalia              | 0.0 - 1.0        |
| Spam           | Promotional, repetitive content      | 0.0 - 1.0        |
| Bullying       | Harassment, cyberbullying            | 0.0 - 1.0        |
| Misinformation | False or misleading content          | 0.0 - 1.0        |

## How It Works

### Processing Pipeline

```mermaid theme={null}
graph TD
    A[Media Input] --> B[Preprocessing]
    B --> C[Feature Extraction]
    C --> D[Model Inference]
    D --> E[Post-processing]
    E --> F[Results & Confidence]
    F --> G[Rule Application]
```

### Model Architecture

1. **Multi-Modal Analysis**: Separate models for different content types
2. **Ensemble Approach**: Multiple models vote for accuracy
3. **Continuous Learning**: Models improve from feedback
4. **Edge Deployment**: Fast, privacy-focused processing

## Configuration

### AI Settings

Configure AI behavior per site or globally:

```json theme={null}
{
  "ai_config": {
    "enabled": true,
    "models": ["violence", "adult", "hate"],
    "confidence_threshold": 0.7,
    "language_models": ["en", "es", "fr"],
    "custom_labels": ["brand_safety", "competitor_content"]
  }
}
```

### Confidence Thresholds

Adjust sensitivity for different use cases:

* **High Sensitivity** (0.3-0.5): Catches more content, more false positives
* **Balanced** (0.5-0.7): Good for most applications
* **Low Sensitivity** (0.7-0.9): Fewer false positives, may miss edge cases

## Custom AI Models

### Training Custom Models

Pixel Patrol supports custom AI models for specific use cases:

1. **Data Collection**: Gather labeled training data
2. **Model Training**: Train on your specific content
3. **Validation**: Test accuracy and performance
4. **Deployment**: Deploy to production

### Use Cases

* **Brand Safety**: Detect competitor logos or products
* **Community Standards**: Enforce specific community guidelines
* **Industry-Specific**: Medical, legal, or financial content
* **Regional Content**: Culturally specific moderation

## Performance

### Speed Metrics

| Content Type    | Average Processing Time | Throughput |
| --------------- | ----------------------- | ---------- |
| Image (\< 5MB)  | 200-500ms               | 1000/min   |
| Video (\< 50MB) | 2-5 seconds             | 100/min    |
| Text (\< 10KB)  | 50-100ms                | 5000/min   |

### Accuracy Metrics

* **Precision**: 94% average across categories
* **Recall**: 91% average across categories
* **F1 Score**: 0.925 overall
* **False Positive Rate**: \< 5%

## Advanced Features

### Multi-Language Support

AI moderation supports 50+ languages:

* Automatic language detection
* Language-specific models
* Cross-language hate speech detection
* Multilingual text extraction

### Contextual Analysis

Beyond simple label detection:

* **Artistic Context**: Distinguishes art from explicit content
* **Medical Context**: Recognizes educational content
* **News Context**: Understands journalistic content
* **Satire Detection**: Identifies humorous intent

### Batch Processing

Process multiple items efficiently:

```typescript theme={null}
// Submit multiple items for batch processing
const items = [
  {
    api_key: "site_xxxxxxxxxxxxxxxxxxxx",
    content_url: "https://example.com/image1.jpg",
    app_media_id: "batch-1",
  },
  {
    api_key: "site_xxxxxxxxxxxxxxxxxxxx",
    content_url: "https://example.com/video1.mp4",
    app_media_id: "batch-2",
  },
  {
    api_key: "site_xxxxxxxxxxxxxxxxxxxx",
    body: "Text to analyze",
    app_media_id: "batch-3",
  },
];

// Submit each item
const results = await Promise.all(
  items.map((item) =>
    fetch("https://api.pixelpatrol.net/functions/v1/submit-media", {
      method: "POST",
      headers: { "Content-Type": "application/json" },
      body: JSON.stringify(item),
    }).then((r) => r.json())
  )
);
```

## Integration

### API Usage

```typescript theme={null}
// Submit for AI moderation
const response = await fetch(
  "https://api.pixelpatrol.net/functions/v1/submit-media",
  {
    method: "POST",
    headers: {
      "Content-Type": "application/json",
    },
    body: JSON.stringify({
      api_key: "site_xxxxxxxxxxxxxxxxxxxx",
      content_url: "https://example.com/image.jpg",
      app_media_id: "media-123",
      metadata: {
        ai_options: {
          models: ["violence", "adult"],
          include_ocr: true,
        },
      },
    }),
  }
);
```

### Real-time Moderation

For live content streams:

* WebSocket connections
* Frame sampling for videos
* Incremental text analysis
* Priority queue processing

## Best Practices

### Optimization

1. **Right-size Media**: Compress before submission
2. **Batch When Possible**: Group related content
3. **Cache Results**: Avoid re-processing identical content
4. **Monitor Performance**: Track processing times

### Accuracy Improvement

1. **Provide Context**: Include metadata when available
2. **Use Feedback**: Report false positives/negatives
3. **Combine with Rules**: Layer AI with business rules
4. **Regular Reviews**: Audit AI decisions periodically

## Limitations

### Known Limitations

* **Context Ambiguity**: May struggle with highly contextual content
* **New Trends**: Requires updates for emerging content types
* **Cultural Nuance**: May need region-specific tuning
* **Adversarial Content**: Can be fooled by intentional manipulation

### Mitigation Strategies

1. **Human Review**: Flag uncertain content for manual review
2. **Continuous Training**: Regular model updates
3. **Feedback Loop**: Learn from moderation decisions
4. **Multiple Signals**: Combine AI with other indicators

## Related Topics

* [Rule-Based Moderation](/features/rule-based-moderation) - Combining AI with rules
* [Moderation Concepts](/concepts/moderation) - Overall moderation flow
* [API Reference](/api-reference/media/moderate) - Technical API details