  A dramatic influx of diffusion-generated images has marked recent years,
posing unique challenges to current detection technologies. While the task of
identifying these images falls under binary classification, a seemingly
straightforward category, the computational load is significant when employing
the "reconstruction then compare" technique. This approach, known as DIRE
(Diffusion Reconstruction Error), not only identifies diffusion-generated
images but also detects those produced by GANs, highlighting the technique's
broad applicability. To address the computational challenges and improve
efficiency, we propose distilling the knowledge embedded in diffusion models to
develop rapid deepfake detection models. Our approach, aimed at creating a
small, fast, cheap, and lightweight diffusion synthesized deepfake detector,
maintains robust performance while significantly reducing operational demands.
Maintaining performance, our experimental results indicate an inference speed
3.2 times faster than the existing DIRE framework. This advance not only
enhances the practicality of deploying these systems in real-world settings but
also paves the way for future research endeavors that seek to leverage
diffusion model knowledge.
