Detecting Video Speed Manipulation

Brian C. Hosler, Matthew C. Stamm; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2020, pp. 670-671

Manipulated videos are frequently an important part of misinformation campaigns. While much attention has recently focused on sophisticated threats such as deepfakes, the majority of videos used in misinformation campaigns have been created using relatively simple manipulations that can be produced by commonly available editing software. One important manipulation that has been used in previous misinformation attempts is altering the speed of a video. In the previous 18 months, widely circulated videos have had their speed manipulated to make Speaker of the House Nancy Pelosi appear disoriented, and to make reporter Jim Acosta appear to act aggressively toward a White House staffer. Currently, however, there are no approaches to accurately detect video speed manipulation that can be deployed at scale. In this paper, we propose new algorithms to detect video speed manipulation and to estimate the rate by which a video's speed has been modified. To do this, we identify a new trace left by video speed manipulation and show how it can be extracted from a video. Our approaches to trace extraction, speed manipulation detection, and manipulation rate estimation are computationally efficient and can be run in a matter of milliseconds. We present experimental results that show that our proposed approach can detect manipulated videos with up to 99% accuracy.