Lens aberrations have previously been used to determine the provenance of an image. However, this is not necessarily unique to an image sensor, as lens systems are often interchanged. Photo-response non-uniformity noise was proposed in 2005 by Lukáš, Goljan and Fridrich as a stochastic signal which describes a sensor uniquely, akin to a “ballistic” fingerprint. This method, however, did not account for additional sources of bias such as lens artefacts and temperature.

In this paper, we propose a new additive signal model to account for artefacts previously thought to have been isolated from the ballistic fingerprint. Our proposed model separates sensor level artefacts from the lens optical system and thus accounts for lens aberrations previously thought to be filtered out. Specifically, we apply standard image processing theory, an understanding of frequency properties relating to the physics of light and temperature response of sensor dark current to classify artefacts. This model enables us to isolate and account for bias from the lens optical system and temperature within the current model.

镜头像差以前曾用于确定图像的出处。但是，由于镜头系统经常互换，因此这不一定是图像传感器特有的。Lukáš，Goljan和Fridrich在2005年提出了一种光响应非均匀噪声，它是一种随机信号，它独特地描述了一个类似于“弹道”指纹的传感器。但是，这种方法没有考虑到其他的偏差来源，例如镜片伪影和温度。

在本文中，我们提出了一个新的加性信号模型，以解决先前被认为与弹道指纹隔离的伪像。我们提出的模型将传感器水平的伪像与镜头光学系统分开，从而解决了以前被认为可以滤除的镜头像差。具体来说，我们采用标准的图像处理理论，即对与光的物理学有关的频率特性以及传感器暗电流的温度响应进行了解，以对伪像进行分类。该模型使我们能够隔离并解决当前模型中透镜光学系统和温度的偏差。