VideoFACT: Detecting Video Forgeries Using Attention, Scene Context, and Forensic Traces

Understand

Fake videos represent an important misinformation threat.

• While existing forensic networks have demonstrated strong performance on image forgeries, recent results reported on the Adobe VideoSham dataset show that these networks fail to identify fake content in videos.
• In this paper, we show that this is due to video coding, which introduces local variation into forensic traces.
• In response, we propose VideoFACT - a new network that is able to detect and localize a wide variety of video forgeries and manipulations.