Department of Computer Science, University of Haifa, Haifa 3498838, Israel.
Sensors (Basel). 2022 Jul 21;22(14):5448. doi: 10.3390/s22145448.
Although camera and sensor noise are often disregarded, assumed negligible or dealt with in the context of denoising, in this paper we show that significant information can actually be deduced from camera noise about the captured scene and the objects within it. Specifically, we deal with depth cameras and their noise patterns. We show that from sensor noise alone, the object's depth and location in the scene can be deduced. Sensor noise can indicate the source camera type, and within a camera type the specific device used to acquire the images. Furthermore, we show that noise distribution on surfaces provides information about the light direction within the scene as well as allows to distinguish between real and masked faces. Finally, we show that the size of depth shadows (missing depth data) is a function of the object's distance from the background, its distance from the camera and the object's size. Hence, can be used to authenticate objects location in the scene. This paper provides tools and insights into what can be learned from depth camera sensor noise.
虽然相机和传感器噪声通常被忽略、认为可以忽略不计，或者在降噪的背景下处理，但在本文中，我们展示了实际上可以从相机噪声中推断出有关拍摄场景和其中物体的重要信息。具体来说，我们处理深度相机及其噪声模式。我们表明，仅从传感器噪声就可以推断出物体的深度和在场景中的位置。传感器噪声可以指示源相机类型，并且在相机类型内，可以指示用于获取图像的特定设备。此外，我们表明，表面上的噪声分布提供了有关场景内光线方向的信息，并且可以区分真实和伪装的面部。最后，我们表明，深度阴影（缺失深度数据）的大小是物体距离背景、距离相机和物体大小的函数。因此，可以用于验证物体在场景中的位置。本文提供了从深度相机传感器噪声中可以学习到的工具和见解。
