可视数据（图像、视频、3D模型）的隐式表达已经成为当前计算机视觉研究的热点，提出了一种新的基于隐式神经表示的模型隐写方案，消息发送方利用神经辐射场（neural radiance fields, NeRF）新视角合成的特性，通过引入一个视角作为密钥，由NeRF模型生成秘密视角图像作为后门，然后利用过拟合的方法训练一个消息提取器，以建立秘密消息和秘密视角图像的一一映射。发送方将训练好的NeRF模型和消息提取器通过公开信道传递给接收方，接收方利用双方共享的密钥，由NeRF模型获得秘密视角下的渲染图像，再通过消息提取器获得秘密消息。而攻击方因无法准确掌握该视角信息，从而无法窃取秘密消息。实验结果证明：所训练的消息提取器实现大容量快速隐写，消息嵌入量达100%，同时NeRF巨大的视角密钥空间保证了该方案的隐蔽性。

The utilization of implicit representation for visual data (such as images, videos, and 3D models) had recently gained significant attention in computer vision research. A novel model steganography scheme with implicit neural representation was proposed. In this scheme, the message sender leveraged neural radiance fields (NeRF) and its viewpoint synthesis capabilities by introducing a viewpoint as a key. The NeRF model generated a secret viewpoint image, which served as a backdoor. Subsequently, a message extractor was trained using overfitting to establish a one-to-one mapping between the secret message and the secret viewpoint image. To transmit the secret message, the sender sent both the trained NeRF model and the message extractor to the receiver through an open communication channel. The receiver employed the shared key to obtain the rendered image from the NeRF model in the secret viewpoint. Consequently, the secret message was extracted by the message extractor. The inherent complexity of the viewpoint information prevented attackers from stealing the secret message accurately. Experimental results demonstrate that the message extractor trained achieves high-capacity steganography with fast performance, achieving a 100% message embedding. Furthermore, the extensive viewpoint key space of NeRF ensures the security of the steganography scheme.