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Image resolution is not based on exact byte-for-byte matches. Burst uses visual clustering to handle the reality that the same meme gets uploaded in many slightly different forms.

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How visual clustering works

Burst groups similar images into visual clusters using perceptual hashing. That allows edited or resized versions of the same meme to resolve together. It also lets a newer meme variant replace an older one naturally when it gains clear support.

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When images update

An image update requires:
  • one visual cluster to become clearly dominant,
  • a clear margin over the current image cluster,
  • persistence over time,
  • and support from multiple distinct deployers.

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Why perceptual hashing

Exact file matching would miss:
  • resized versions of the same image,
  • images with minor edits or overlays,
  • re-exports at different quality levels,
  • and cropped or padded variants.
Perceptual hashing compares the visual content of images rather than their raw bytes. This makes clustering robust to common image variations.

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Anti-spam protection

The multi-deployer requirement prevents a single actor from flooding an identity cluster with a new image to force an override. Combined with the dominance and persistence requirements, this makes image manipulation through spam significantly harder.