DistilDIRE：一種小型、快速、便宜且輕量級的擴散合成深度偽造檢測

摘要

近年來，擴散生成的圖像大幅增加，對當前的檢測技術提出了獨特挑戰。儘管識別這些圖像的任務屬於二元分類，看似簡單的類別，但當採用“重建再比較”技術時，計算負載是顯著的。這種方法被稱為DIRE（擴散重建誤差），不僅可以識別擴散生成的圖像，還可以檢測由GANs生成的圖像，突顯了該技術的廣泛應用性。為了應對計算挑戰並提高效率，我們提出提煉擴散模型中嵌入的知識，以開發快速的深度偽造檢測模型。我們的方法旨在創建一個小型、快速、便宜且輕量級的擴散合成深度偽造檢測器，保持強大的性能同時顯著降低運行需求。通過保持性能，我們的實驗結果顯示推理速度比現有的DIRE框架快3.2倍。這一進展不僅增強了在現實世界環境中部署這些系統的實用性，還為未來旨在利用擴散模型知識的研究努力鋪平了道路。

English

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：一種小型、快速、便宜且輕量級的擴散合成深度偽造檢測

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

摘要

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