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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.

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AI Capabilities

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Content Analysis

Visual Analysis

  • Object detection - Scene understanding - Text extraction (OCR) - Face detection - Brand/logo recognition

Context Understanding

  • Semantic analysis - Context awareness - Cultural sensitivity - Sarcasm detection - Intent classification

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Detection Categories

Our AI models detect multiple content categories:
CategoryDescriptionConfidence Range
ViolenceGraphic violence, weapons, gore0.0 - 1.0
AdultNudity, sexual content0.0 - 1.0
HateDiscriminatory content, hate symbols0.0 - 1.0
Self-HarmContent promoting self-injury0.0 - 1.0
DrugsDrug use, paraphernalia0.0 - 1.0
SpamPromotional, repetitive content0.0 - 1.0
BullyingHarassment, cyberbullying0.0 - 1.0
MisinformationFalse or misleading content0.0 - 1.0

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How It Works

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Processing Pipeline

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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

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Configuration

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AI Settings

Configure AI behavior per site or globally: 
{
  "ai_config": {
    "enabled": true,
    "models": ["violence", "adult", "hate"],
    "confidence_threshold": 0.7,
    "language_models": ["en", "es", "fr"],
    "custom_labels": ["brand_safety", "competitor_content"]
  }
}

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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

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Custom AI Models

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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

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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

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Performance

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Speed Metrics

Content TypeAverage Processing TimeThroughput
Image (< 5MB)200-500ms1000/min
Video (< 50MB)2-5 seconds100/min
Text (< 10KB)50-100ms5000/min

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Accuracy Metrics

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

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Advanced Features

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Multi-Language Support

AI moderation supports 50+ languages:
  • Automatic language detection
  • Language-specific models
  • Cross-language hate speech detection
  • Multilingual text extraction

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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

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Batch Processing

Process multiple items efficiently: 
// 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())
  )
);

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Integration

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API Usage

// 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,
        },
      },
    }),
  }
);

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Real-time Moderation

For live content streams:
  • WebSocket connections
  • Frame sampling for videos
  • Incremental text analysis
  • Priority queue processing

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Best Practices

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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

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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

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Limitations

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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

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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