Use Case

Re-upload detection
for social platforms

Banned images and videos reappear minutes after removal — re-encoded, cropped, or AI-upscaled to slip past exact-match filters. At millions of uploads per day, manual review cannot keep up.

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The problem

  • Millions of uploads per day make manual re-review impossible — enforcement must be automated.
  • Bad actors re-encode video and re-save images specifically to defeat SHA-256 hash blocklists.
  • CSAM-adjacent and harassment content keeps circulating because pixel-level edits invalidate existing fingerprints.
  • Every hour of undetected re-distribution increases legal exposure and erodes user trust.

73%

of re-uploads happen within 24 hours of original removal

12×

more re-upload attempts per removed piece of content vs 3 years ago

< 1%

of re-uploads are byte-identical — the rest evade SHA-256

How Rechase helps

Sub-50ms at any scale

Three detection layers run in parallel — SHA-256, perceptual hashing, and CLIP semantic embeddings — returning a verdict before the upload reaches storage. No throughput ceiling.

Survives every re-encode

CLIP embeddings capture semantic content, not pixels. A video re-encoded to H.265, colour-graded, and letterboxed still matches the original with 90%+ confidence.

Audit trail for legal teams

Every block is logged with fingerprint scores, detection method, and a link to the original removal order. Exportable for CSAM reporting and regulatory compliance.

Detection methods

What each layer catches specifically on social platforms.

MethodWhat it catches on social platformsLatency
SHA-256Identical re-posts — users who copy-paste files directly without editing< 1ms
pHashCropped screenshots, watermark removals, aspect-ratio changes, colour filter additions< 5ms
CLIPRe-encoded videos, AI upscales, deepfake variants, style-transferred images with same harmful content30–50ms

Ready to protect your
social platform?

30-minute call. We walk through your upload volume, existing hash blocklist, and estimate time-to-integration.

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