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AI detection technology

Powered by advanced machine learning, this API analyzes images to determine the probability of AI generation with high accuracy.
The AI Image Classifier is a detection API that analyzes images to determine whether they were created by artificial intelligence or are authentic photographs. Submit any image and receive a confidence score indicating the probability of AI generation vs. natural origin. The API returns instant results, making it ideal for real-time content moderation and verification workflows.

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Key capabilities

  • Binary classification: Returns probability scores for both ai and not_ai categories
  • High accuracy detection: Advanced ML model trained on diverse AI-generated and authentic images
  • Multiple input formats: Accepts base64-encoded images, URLs, or binary data
  • Instant response: Synchronous API with immediate results (no task polling required)
  • Confidence scoring: Probability values from 0 to 1 for precise threshold-based decisions
  • Format support: Works with common image formats (JPEG, PNG, WebP, GIF)

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Use cases

  • Content moderation: Automatically flag potentially AI-generated uploads on user platforms
  • Editorial verification: Verify image authenticity before publication in news or media
  • Stock image curation: Filter AI-generated content from authentic photography collections
  • Social media compliance: Detect synthetic content for platform policy enforcement
  • Academic integrity: Identify AI-generated images in research or educational submissions
  • Legal and forensic analysis: Support authenticity verification for evidence documentation

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Classify images with AI Image Classifier

Submit an image to analyze whether it was generated by AI. The API returns probability scores instantly without requiring task polling.

POST /v1/ai/classifier/image

Analyze an image for AI-generation probability

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Parameters

ParameterTypeRequiredDefaultDescription
imagestringYes-Image to analyze: base64-encoded string, URL, or binary data

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Response

The API returns an array of classification results with probability scores:
FieldTypeDescription
class_namestringClassification category: ai or not_ai
probabilitynumberConfidence score from 0 to 1 (higher = more confident)
Example response: 
{
  "data": [
    {
      "class_name": "not_ai",
      "probability": 0.9489
    },
    {
      "class_name": "ai",
      "probability": 0.0511
    }
  ]
}

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Frequently Asked Questions

The AI Image Classifier is a detection API that uses machine learning to analyze visual patterns in images that are characteristic of AI generation. It examines features like texture consistency, artifact patterns, and statistical properties to determine whether an image was created by AI tools (like Midjourney, DALL-E, or Stable Diffusion) or is an authentic photograph. The API returns probability scores for both categories.
The classifier accepts common image formats including JPEG, PNG, WebP, and GIF. Images can be provided as base64-encoded strings, direct URLs, or binary data in the request body.
The API returns two probability scores that sum to approximately 1.0. A not_ai probability of 0.95 means the model is 95% confident the image is authentic. For content moderation, you can set thresholds based on your requirements - for example, flag images where ai probability exceeds 0.7 for review.
The classifier is trained on a diverse dataset of AI-generated images from various models and authentic photographs. Accuracy depends on the image type and generation method. The probability scores help you make threshold-based decisions appropriate for your use case. For critical applications, consider combining with manual review.
Rate limits vary by subscription tier. See Rate Limits for current limits and how to handle rate-limited requests.
See the Pricing page for current rates and subscription options.
The classifier is optimized for detecting fully AI-generated images. Partially edited images (AI inpainting on real photos, filters, or composites) may produce mixed results. For best accuracy, use on complete images rather than crops or heavily processed versions.

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Best practices

  • Image quality: Submit high-resolution images when possible for more accurate analysis
  • Threshold tuning: Set classification thresholds based on your false-positive tolerance
  • Batch processing: For high-volume workflows, implement request queuing to respect rate limits
  • Human review: Use API scores to prioritize manual review rather than as sole decision criteria
  • Error handling: Implement retry logic with exponential backoff for 503 errors
  • Caching: Cache results for identical images to reduce API calls and costs
  • Mystic: Generate high-quality AI images with Freepik’s proprietary model
  • Image Upscaler: Enhance image resolution with AI upscaling
  • Background Remover: Remove or replace image backgrounds