We introduce a new open set video forensics problem called video camera model verification. The video camera model verification task is to determine if two query videos were captured by the same camera model. Importantly, verification must be reliable on videos from camera models unknown to the investigator, referred to the as the open set scenario. While researchers have considered other open set problems for digital images, video forensics introduces unique challenges. In this work we propose a new, video-specific system for open set verification of camera models. To do this, we design a deep-learning based system that 1) uses a CNN to extract expressive deep features from video patches, 2) compares pairs of these features using a similarity network, and 3) fuses multiple comparisons to produce a video-level verification decision. We experimentally show that this technique accurately verifies the source camera model of videos in open set scenarios.