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DistilDIRE: A Small, Fast, Cheap and Lightweight Diffusion Synthesized Deepfake Detection

Yewon Lim, Changyeon Lee, Aerin Kim, Oren Etzioni

TL;DR

Diffusion-generated media poses detection challenges due to the computational heft of state-of-the-art approaches like DIRE. The authors present DistilDIRE, a teacher-student framework that distills diffusion-model knowledge into a compact detector by fusing the original image with the diffusion process's initial noise and training with both binary classification loss and a distillation loss. DistilDIRE achieves near-DIRE detection performance while reducing inference-cost by about 97%, delivering approximately 3.2× faster inference on standard hardware. The approach also demonstrates robustness to GAN-generated images, suggesting broad applicability. This work enables practical, real-time diffusion-synthesized deepfake detection and lays groundwork for future diffusion-knowledge transfer.

Abstract

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.

DistilDIRE: A Small, Fast, Cheap and Lightweight Diffusion Synthesized Deepfake Detection

TL;DR

Diffusion-generated media poses detection challenges due to the computational heft of state-of-the-art approaches like DIRE. The authors present DistilDIRE, a teacher-student framework that distills diffusion-model knowledge into a compact detector by fusing the original image with the diffusion process's initial noise and training with both binary classification loss and a distillation loss. DistilDIRE achieves near-DIRE detection performance while reducing inference-cost by about 97%, delivering approximately 3.2× faster inference on standard hardware. The approach also demonstrates robustness to GAN-generated images, suggesting broad applicability. This work enables practical, real-time diffusion-synthesized deepfake detection and lays groundwork for future diffusion-knowledge transfer.

Abstract

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 Structure (16 sections, 7 equations, 1 figure, 3 tables)

This paper contains 16 sections, 7 equations, 1 figure, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Denoising Diffusion Implicit Models
  4. Diffusion Synthesized Image Detection
  5. Methodology
  6. Architecture
  7. Noise Extraction
  8. Experiment
  9. Experiments Setup
  10. Datasets and Pre-processing
  11. Evaluation
  12. Results
  13. Deepfake Detection Performance
  14. Inference Time Comparison
  15. Ablation Study
  16. ...and 1 more sections

Figures (1)

  • Figure 1: Overview of our framework.