Detect duplicate, near-duplicate, re-encoded, mirrored, and cropped video copies at scale with AI-powered visual and audio fingerprinting. F1 0.995 benchmark accuracy.
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Maker
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Hey Product Hunt! š
We built FrameGuard after working with a platform that had a serious re-upload problem ā users constantly re-encoding, cropping, and mirroring videos to dodge detection.
pHash failed immediately on anything real. We ended up building on CLIP visual embeddings + CNN14 audio fingerprinting, with a lot of painful iteration on the scoring formula ā about 20 versions before we hit numbers we were happy with.
A few things that surprised us along the way:
ā Audio-only matching caused false positive explosion (trending background music matches unrelated videos). Audio is now corroborating-only.
ā A chain ratio penalty we thought was clever caused 58.9% recall regression. Removed entirely.
ā Structural score softening introduced 15 extra false positives per run. Also removed.
Current benchmark: F1 0.995, 99.7% precision, 99.4% recall.
Free tier is available ā no credit card required. Happy to answer any questions about how the detection works.