Data-Driven Detection of Video Forgery for Secured Microcontrollers: Dataset and Method

Abstract

The microcontrollers in a camera often capture videos at a low frame rate due to limited processing capability. To satisfy the requirement of high quality of service, low-frame-rate videos are often forged as the high-frame-rate ones by the Frame Rate Up-Conversion (FRUC) operation. Therefore, detecting the existence of FRUC has become a necessary job for secured microcontrollers. In this paper, we propose a data-driven detection to identify whether a video is forged by FRUC. The core of detection is the creation of a large-scale video dataset VifFRUC (Videos forged by FRUC). Various types of forged videos can continue to be added into VifFRUC, making the detection more universal and robust. To match with VifFRUC, we have also designed a neural network, which trains a number of Long Short Term Memory (LSTM) units in parallel to learn the data-driven detection. The parallel LSTM structure of network can continually adapt to the newly added FRUC methods in VifFRUC. Extensive experiments on VifFRUC demonstrate the effectiveness of data-driven detection for FRUC, resulting in the security improvement of microcontrollers.