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Models / Duplicate Detection

Image Duplicate and Near-Duplicate Detection

Overview

The Near-Duplicate Detection model is used to identify images that are so called duplicates or near-duplicates. It can be used across the following use-cases:

  • Image Blacklists and Disallow lists: Blacklist images and prevent them from (re)appearing on your site or app. For instance copyrighted images, illegal images, previously removed images.
  • Spam & theft prevention: Detect spammy and unwanted behaviors. Prevent users from submitting the same image multiple times, and from submitting other users' photos.

Video duplicate detection is also available, see the Video Duplicate Detection guide.

What the model catches

Duplicate detection works across all types of images: natural photos, drawings, screenshots, anime etc.

Duplicates are detected across a wide range of transformations and modifications, many of which are typically used to try to evade duplicate detection. Examples:

Original image

Resolution, size and format changes

  • Downscaling and upscaling
  • DPI/Resolution changes
  • Re-encoding or format conversion (e.g. JPEG, PNG, WEBP...)

Text overlays

  • Text overlays and added captions
  • Stickers, logos, watermarks

Image overlays

  • Large image overlays obscuring parts of the original image
  • Emojis, shapes and other graphical overlays

Cropping and reframing

  • Tight crops, letterboxing, added borders/frames
  • Partial views of the original

Collage

  • When the source image appears inside a multi-image layout
  • Split-screen layouts

Blur

  • Strong gaussian blur, motion blur, defocus...
  • Pixelation

Image mixing

  • When the source image is blended with other images or backgrounds

Color changes and filters

  • Grayscale, channel drops, saturation/brightness/contrast/hue changes
  • Color filters and tints

Geometric transformations, stretching, flipping

  • Aspect ratio changes, stretching, perspective tweaks
  • Rotations
  • Horizontal and vertical flips

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