A Deep Learning Based Digital Forensic Solution to Blind Source Identification of Facebook Images

Abstract

Source Camera Identification is a digital forensic way of attributing a contentious image to its authentic source, especially used in legal application domains involving terrorism, child pornography etc. The state–of–the–art source camera identification techniques, however, are not suitable to work with images downloaded from online social networks. This is because, online social networks impart specific image artefacts, due to proprietary image compression requirements for storage and transmission, which prevents accurate forensic source investigations. Moreover, each social network has its own compression standards, which are never made public due to ethical issues. This makes source identification task even more difficult for forensic analysts. In present day and age, where there is abundant use of social networks for image transmission, it is high time that source camera identification with images downloaded from social networks, be efficiently addressed. In this paper, we propose a deep learning based digital forensic technique for source camera identification, on images downloaded from Facebook. The proposed deep learning technique is adapted from the popular ResNet50 network, which majorly consists of convolutional layers and a few pooling layers. Our experimental results prove that the proposed technique outperforms the traditional source camera identification methods.