How We Made Image Copy-Detection ~1,000,000 Faster — by Refusing to Look at Pixels
Structural hashing for pixel art: a 1024-byte key that answers 'is this the same artwork?' in 115 nanoseconds — and then tells you exactly what was edited. Nine days, or one second. That is the real distance between the naive answer and the structural one — measured, on a single CPU core, for the same question asked of the same ten million images. This article is the story of that gap, and of the…
Pixagram is a pixel-art social network featuring an on-chain marketplace for artworks. To ensure originality and detect near-duplicates, the platform employs a structural hashing method to compare artworks instead of pixel-by-pixel comparison. By reducing the image to a canonical form and using Gray code for palette surgery, the hash can detect edits such as palette changes, size variations, and pixel alterations.
This structural hashing technique allows for a 115 nanosecond comparison of two 1024-byte keys instead of an 80 millisecond raw diff. The hash approach results in a 700,000x speed improvement when checking millions of artworks, making it a significant advancement in copy-detection for this niche digital art community.
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