  Recent advances in neural rendering imply a future of widespread visual data
distributions through sharing NeRF model weights. However, while common visual
data (images and videos) have standard approaches to embed ownership or
copyright information explicitly or subtly, the problem remains unexplored for
the emerging NeRF format. We present StegaNeRF, a method for steganographic
information embedding in NeRF renderings. We design an optimization framework
allowing accurate hidden information extractions from images rendered by NeRF,
while preserving its original visual quality. We perform experimental
evaluations of our method under several potential deployment scenarios, and we
further discuss the insights discovered through our analysis. StegaNeRF
signifies an initial exploration into the novel problem of instilling
customizable, imperceptible, and recoverable information to NeRF renderings,
with minimal impact to rendered images. Project page:
https://xggnet.github.io/StegaNeRF/.
