The goal of temporal image forensic is to approximate the age of a digital image relative to images from the same device. Usually, this is based on traces left during the image acquisition pipeline. For example, several methods exist that exploit the presence of in-field sensor defects for this purpose. In addition to these 'classical' methods, there is also an approach in which a Convolutional Neural Network (CNN) is trained to approximate the image age. One advantage of a CNN is that it independently learns the age features used. This would make it possible to exploit other (different) age traces in addition to the known ones (i.e., in-field sensor defects). In a previous work, we have shown that the presence of strong in-field sensor defects is irrelevant for a CNN to predict the age class. Based on this observation, the question arises how device (in)dependent the learned features are. In this work, we empirically asses this by training a network on images from a single device and then apply the trained model to images from different devices. This evaluation is performed on 14 different devices, including 10 devices from the publicly available 'Northumbria Temporal Image Forensics' database. These 10 different devices are based on five different device pairs (i.e., with the identical camera model).

数字图像的时间取证目标是相对于同一设备的图像近似估计其年龄。传统的方法是基于图像采集过程中留下的痕迹，而卷积神经网络（CNN）作为一种新方法，可以独立学习用于估计图像年龄的特征，并可以利用与在场传感器缺陷不同的其他年龄痕迹。本研究通过训练单一设备的网络，然后将训练的模型应用于不同设备的图像来评估所学特征的设备相关性。

深度学习图像年龄近似的设备(不)依赖性