PLUS Temporal Image Forensics Dataset
Robert Jöchl, Andreas Uhl · 2024
In the field of temporal image forensics, the main objective is to approximate the age of a digital image relative to images from the same device. For this purpose, classical methods exist where age inference is based on the presence of a hidden age signal (i.e., in-field sensor defects). In contrast to these classic methods, there exists also a method for image age approximation based on a Convolutional Neural Network (CNN). However, exploiting neural networks for age approximation carries the risk of learning non-age-related features to predict the age class. Usually, images taken in close temporal proximity (i.e., belonging to the same age class) share common scene properties (aka content bias), which can be exploited by the neural network. In this work, a new temporal image forensics dataset is proposed where content bias is limited. This dataset could help to, (i) develop deep learning based age approximation methods (ii) facilitate the discovery of new (unknown) age traces, (iii) assess the impact of content bias on existing age approximation methods and (iv) develop and verify new eXplainable Artificial Intelligence methods. A realization of the dataset as a benchmark for robustness against content bias is demonstrated in this work.