Source Camera Verification from Strongly Stabilized Videos

The in-camera image stabilization technology deployed by most cameras today poses one of the most significant challenges to photo-response non-uniformity based source camera attribution from videos. When performed digitally, stabilization involves cropping, warping, and inpainting of video frames to eliminate unwanted camera motion. Hence, successful attribution requires inversion of these transformations in a blind manner. To address this challenge, we introduce a source camera verification method for videos that takes into account spatially variant nature of stabilization transformations and assumes a larger degree of freedom in their search. Our method identifies transformations at a sub-frame level and incorporates a number of constraints to validate their correctness. The method also adopts a holistic approach in countering disruptive effects of other video generation steps, such as video coding and downsizing, for more reliable attribution. Tests performed on one public and two custom datasets show that proposed method is able to verify the source of 23-40% of videos that underwent stronger stabilization without a significant impact on false attribution rate