  The utilization of implicit representation for visual data (such as images,
videos, and 3D models) has recently gained significant attention in computer
vision research. In this letter, we propose a novel model steganography scheme
with implicit neural representation. The message sender leverages Neural
Radiance Fields (NeRF) and its viewpoint synthesis capabilities by introducing
a viewpoint as a key. The NeRF model generates a secret viewpoint image, which
serves as a backdoor. Subsequently, we train a message extractor using
overfitting to establish a one-to-one mapping between the secret message and
the secret viewpoint image. The sender delivers the trained NeRF model and the
message extractor to the receiver over the open channel, and the receiver
utilizes the key shared by both parties to obtain the rendered image in the
secret view from the NeRF model, and then obtains the secret message through
the message extractor. The inherent complexity of the viewpoint information
prevents attackers from stealing the secret message accurately. Experimental
results demonstrate that the message extractor trained in this letter achieves
high-capacity steganography with fast performance, achieving a 100\% accuracy
in message extraction. Furthermore, the extensive viewpoint key space of NeRF
ensures the security of the steganography scheme.
