Apart from In-field Sensor Defects, are there Additional Age Traces Hidden in a Digital Image?

Abstract

Approximating the age of a digital image based on traces left during the acquisition pipeline is at the core of temporal image forensics. Well-known and investigated traces are those caused by in-field sensor defects. The presence of these defects is exploited in two available age approximation methods. A very recent approach in this context, however, trains a Convolutional Neural Network for age approximation. A Convolutional Neural Network independently learns the classification features used. In this context, the following questions arise: how relevant is the presence of strong in-field sensor defects, or does the Convolutional Neural Network learn other age-related features (apart from strong in-field sensor defects)? We investigate these questions systematically on the basis of several experiments in this paper. Furthermore, we analyse whether the learned features are position invariant. This is important since selecting the right input patches is crucial for training a Convolutional Neural Network.