Flymark embeds invisible watermarks in 3d gaussian splatting files
FlyMark: Training-Free Invisible Watermarking of 3D Gaussian Splatting via a Fruit Fly Connectome
Computer Vision and Pattern Recognition
Summary
Digital 3D scenes can be copied and shared easily, making it hard to prove who owns them. The authors created FlyMark, a method that hides ownership information directly inside the 3D scene's data without needing additional tools or training. It uses a pattern inspired by a fruit fly's eye structure to encode secret messages invisible to viewers but recoverable later. This watermark doesn’t change the scene’s appearance or shape and can be checked even after the original tools are lost.
What this means in practice
- •For 3d graphics developers: Embed ownership proofs directly into 3D Gaussian Splatting files to protect digital assets without altering scene appearance.
- •For digital asset managers: Verify the authenticity and rightful ownership of 3D scenes even after format changes or compression by extracting embedded watermarks.
Authors
Ziyuan Luo, Haoliang Li, Renjie Wan
Abstract
A trained 3D Gaussian Splatting (3DGS) scene ships as a portable parameter array that can be copied, pruned, requantized, or repackaged outside its training pipeline, so ownership evidence is most useful when it lives in the released parameters and remains checkable long after the embedding tooling is gone. Existing 3DGS watermarks typically tie embedding or extraction to scene optimization, a learned decoder, or rendered views, so the evidence survives only as long as a second trained artifact does. FlyMark instead writes a keyed message into the parameters a 3DGS file already stores. Its carrier directions are derived from the photoreceptors of a published connectome, a citable versioned artifact that fixes the geometry exhaustively and leaves nothing to tune per scene. A virtual observer reads cone-wise apparent luminance along a scene-normalized orbit from stored centers, colors, and opacities; a keyed dithered quantization-index-modulation code replicates each message bit across these observations; and one sparse bounded least-squares solve realizes the targets through achromatic shifts of existing degree-zero colors under a hard per-channel linear-RGB bound. All geometry and higher-order appearance parameters are preserved bit-identically, and extraction needs only cone queries, rounding, and majority voting. Under a model-domain threat model on synthetic and real scenes, FlyMark attains high clean bit accuracy and visual fidelity while cleanly separating matched from wrong keys.