Built for every modification the internet can throw at your art.

The artworks above are from the game Glitch, whose studio released its art into the public domain (CC0). They are part of the corpus Candela trains on. Registered work is never used for training unless its artist opts in.

PreviewCandela is still in development, with capabilities current as of August 23, 2026.

Candela is an image recognition model built for a single question: are these two images the same work? Crops, filters, screenshots and recompression do not change the answer, and holding up against AI edits is a design goal we are actively measuring against. Candela powers every verification on Lantern, and it is built to stand on its own.

Seeing is believing

Proof you can look at.

A cropped and recompressed copy, matched back to its original. The artwork is a public-domain piece of digital game art. Each line is a point the in-development verification stage confirmed in both images.

Public-domain digital artwork from the game Glitch (CC0)Geometric verification prototype, run 2026-08-09
A cropped copy of a digital artwork above its original, connected by red lines showing verified matching points between them

Each dot is a small detail, a brushstroke or an edge, found in both images. Each line ties the detail in the copy to the same detail in the original. When this many independent points agree on one alignment, the match is confirmed by geometry. This stage is not yet part of the served pipeline.

Simple but powerful

Built for art by design.

Candela is specialized for digital art. The version serving traffic today, candela-2-pilot, is a single art-specialized model fine-tuned on openly licensed art; it replaced the three-model ensemble that served before it. Every match carries a similarity score whose thresholds are tuned against measured outcomes, and a point-by-point geometric verification stage is in development.

83%

of altered copies caught by the art-specialized model serving traffic today, at a 1% false-alarm budget

Internal evaluation