Device (In)Dependence of Deep Learning-based Image Age Approximation
Robert Jöchl Andreas Uhl
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).