论文标题
Steganerf：将无形信息嵌入神经辐射场
StegaNeRF: Embedding Invisible Information within Neural Radiance Fields
论文作者
论文摘要
神经渲染的最新进展意味着通过共享NERF模型权重的广泛视觉数据分布的未来。但是，尽管常见的视觉数据（图像和视频）具有明确或巧妙的嵌入所有权或版权信息的标准方法，但对于新兴的NERF格式，该问题仍未探索。我们介绍Steganerf，这是一种嵌入NERF渲染中的地理信息的方法。我们设计了一个优化框架，可从NERF呈现的图像中提取准确的隐藏信息，同时保留其原始的视觉质量。我们在几种潜在的部署方案下对我们的方法进行实验评估，并进一步讨论通过分析发现的见解。 Steganerf表示对新的问题的最初探索，即向NERF渲染灌输可定制，不可察觉和可回收的信息，对渲染图像的影响很小。项目页面：https：//xggnet.github.io/steganerf/。
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/.
