Title: Scaling Laws for Deepfake Detection
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: scaling law, deepfake detection, dataset

Abstract: This paper presents a systematic study of scaling laws for the deepfake detection task. Specifically, we analyze the model performance against the number of real image domains, deepfake generation methods, and training images. Since no existing dataset meets the scale requirements of this research, we construct ScaleDF, the largest dataset to date in this field, which contains over 5.8 million real images from 51 different datasets (domains) and more than 8.8 million fake images generated by 102 deepfake methods. Using ScaleDF, we observe power-law scaling similar to that shown in large language models (LLMs). Specifically, the average detection error follows a predictable power-law decay as either the number of real domains or the number of deepfake methods increases. This key observation not only allows us to forecast the number of additional real domains or deepfake methods required to reach a target performance, but also inspires us to counter the evolving deepfake technology in a data-centric manner. Beyond this, we further examine the role of pre-training and data augmentations in deepfake detection under scaling, as well as the limitations of scaling itself.

TL;DR: We observe that the detection error follows a power-law decay as the number of real domains or deepfake methods increases, similar to the scaling behaviors observed in Large Language Models (LLMs).