DistilDIRE: A Small, Fast, Cheap and Lightweight Diffusion Synthesized
  Deepfake Detection
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.
66a2cb4884301ad74809dbac
Yewon Lim
claimed_verified
2024-10-14T08:33:55.245Z
64c224cad1ca220e304248bb
/avatars/2e7ff92cec355db17a83dfc1cac6c2e8.svg
Yewon Lim
user
yevvonlim
66a2cb4884301ad74809dbad
Changyeon Lee
66a2cb4884301ad74809dbae
Aerin Kim
claimed_verified
2024-07-26T07:40:21.978Z
630629d9660f01f150a110d3
https://cdn-avatars.huggingface.co/v1/production/uploads/630629d9660f01f150a110d3/-9_cmjec1aEdX_1BKosCk.png
aerin kim
user
aerinkim
66a2cb4884301ad74809dbaf
Oren Etzioni
DistilDIRE: A Small, Fast, Cheap and Lightweight Diffusion Synthesized
  Deepfake Detection
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.
Knowledge distillation from diffusion models is used to develop a more efficient and lightweight deepfake detector compared to the DIRE framework.
diffusion-generated images binary classification reconstruction then compare DIRE diffusion models deepfake detection knowledge distillation rapid detection