Towards Open Set Camera Model Identification Using a Deep Learning Framework

Abstract

Existing forensic camera model identification algorithms can be trained to accurately distinguish between a set of known camera models. In reality, however, an investigator may be confronted with an image that was not captured by one of these known models. If this happens, existing algorithms will associate this image with one of the known camera models. This is known as the open set problem. In this paper, we propose two different approaches to address the open set problem for camera model identification. To do this, we use a CNN to learn a set of deep forensic features. Our first approach replaces the CNN's classifier with a confidence score mapping which it thresholds to detect unknown models. Our second approach uses a set of `known unknown' models to train a new classifier to identify unknown camera models. Experiments show that we can detect unknown camera models with a 97.74% accuracy.