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