  In the context of temporal image forensics, it is not evident that a neural
network, trained on images from different time-slots (classes), exploits solely
image age related features. Usually, images taken in close temporal proximity
(e.g., belonging to the same age class) share some common content properties.
Such content bias can be exploited by a neural network. In this work, a novel
approach is proposed that evaluates the influence of image content. This
approach is verified using synthetic images (where content bias can be ruled
out) with an age signal embedded. Based on the proposed approach, it is shown
that a deep learning approach proposed in the context of age classification is
most likely highly dependent on the image content. As a possible
countermeasure, two different models from the field of image steganalysis,
along with three different preprocessing techniques to increase the
signal-to-noise ratio (age signal to image content), are evaluated using the
proposed method.
