  The identification of source cameras from videos, though it is a highly
relevant forensic analysis topic, has been studied much less than its
counterpart that uses images. In this work we propose a method to identify the
source camera of a video based on camera specific noise patterns that we
extract from video frames. For the extraction of noise pattern features, we
propose an extended version of a constrained convolutional layer capable of
processing color inputs. Our system is designed to classify individual video
frames which are in turn combined by a majority vote to identify the source
camera. We evaluated this approach on the benchmark VISION data set consisting
of 1539 videos from 28 different cameras. To the best of our knowledge, this is
the first work that addresses the challenge of video camera identification on a
device level. The experiments show that our approach is very promising,
achieving up to 93.1% accuracy while being robust to the WhatsApp and YouTube
compression techniques. This work is part of the EU-funded project 4NSEEK
focused on forensics against child sexual abuse.
