  With the advance in user-friendly and powerful video editing tools, anyone
can easily manipulate videos without leaving prominent visual traces.
Frame-rate up-conversion (FRUC), a representative temporal-domain operation,
increases the motion continuity of videos with a lower frame-rate and is used
by malicious counterfeiters in video tampering such as generating fake
frame-rate video without improving the quality or mixing temporally spliced
videos. FRUC is based on frame interpolation schemes and subtle artifacts that
remain in interpolated frames are often difficult to distinguish. Hence,
detecting such forgery traces is a critical issue in video forensics. This
paper proposes a frame-rate conversion detection network (FCDNet) that learns
forensic features caused by FRUC in an end-to-end fashion. The proposed network
uses a stack of consecutive frames as the input and effectively learns
interpolation artifacts using network blocks to learn spatiotemporal features.
This study is the first attempt to apply a neural network to the detection of
FRUC. Moreover, it can cover the following three types of frame interpolation
schemes: nearest neighbor interpolation, bilinear interpolation, and
motion-compensated interpolation. In contrast to existing methods that exploit
all frames to verify integrity, the proposed approach achieves a high detection
speed because it observes only six frames to test its authenticity. Extensive
experiments were conducted with conventional forensic methods and neural
networks for video forensic tasks to validate our research. The proposed
network achieved state-of-the-art performance in terms of detecting the
interpolated artifacts of FRUC. The experimental results also demonstrate that
our trained model is robust for an unseen dataset, unlearned frame-rate, and
unlearned quality factor.
