DistilDIRE: A Small, Fast, Cheap and Lightweight Diffusion Synthesized Deepfake Detection
Yewon Lim, Changyeon Lee, Aerin Kim, Oren Etzioni
2024-07-25
Summary
This paper introduces DistilDIRE, a new and efficient method for detecting deepfakes created by diffusion models. It focuses on making the detection process faster and less resource-intensive while maintaining accuracy.
What's the problem?
As technology improves, more realistic deepfakes are being created using diffusion models, which makes it harder to identify fake images and videos. Current detection methods, like the DIRE (Diffusion Reconstruction Error) technique, can be very slow because they require a lot of computational power. This slow performance makes it difficult to use these methods in real-world situations where quick decisions are needed.
What's the solution?
To solve this problem, the researchers developed DistilDIRE, which uses knowledge distillation to create a smaller and faster model for detecting deepfakes. Instead of relying on heavy computations, DistilDIRE efficiently extracts important features from pre-trained models and uses them to quickly determine whether an image is real or fake. The results show that DistilDIRE can perform detection tasks 3.2 times faster than the existing DIRE framework while still being accurate.
Why it matters?
This research is important because it provides a practical tool for identifying deepfakes in various applications, such as social media, news, and security. By improving the speed and efficiency of deepfake detection, DistilDIRE can help combat misinformation and protect people from being misled by fake media.
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.