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).

翻译：时间图像取证的目标是估计数字图像相对于同一设备拍摄的其他图像的年龄。通常，这基于图像采集过程中遗留的痕迹。例如，已有多种方法利用传感器现场缺陷来实现这一目的。除了这些“经典”方法外，还有一种方法通过训练卷积神经网络（CNN）来估计图像年龄。CNN的优势在于能够自主学习用于年龄预测的特征，这使得除了已知的现场传感器缺陷等痕迹外，还能利用其他（不同的）年龄痕迹。在先前的工作中，我们证明了强现场传感器缺陷的存在对于CNN预测年龄类别无关紧要。基于这一观察，一个关键问题浮现：学习到的特征具有多大的设备（非）依赖性？在本研究中，我们通过训练一个在单一设备图像上的网络，并将该模型应用于不同设备图像进行实证评估。该评估涉及14种不同设备，其中包括来自公开数据集“诺森比亚时间图像取证数据库”的10种设备。这10种设备基于五组不同的设备对（即相同相机型号）。