Scaling Laws for Deepfake Detection
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 for 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 examine the role of
pre-training and data augmentations in deepfake detection under scaling, as
well as the limitations of scaling itself.
6900c98e646208eac0d1f035
Wenhao Wang
6900c98e646208eac0d1f036
Longqi Cai
6900c98e646208eac0d1f037
Taihong Xiao
6900c98e646208eac0d1f038
Yuxiao Wang
6900c98e646208eac0d1f039
Ming-Hsuan Yang
Scaling Laws for Deepfake Detection
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 for 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 examine the role of
pre-training and data augmentations in deepfake detection under scaling, as
well as the limitations of scaling itself.
Research on scaling laws in deepfake detection using the largest dataset to date reveals power-law scaling similar to large language models, enabling performance forecasting and data-centric countermeasures against evolving deepfake technology.
deepfake detection scaling laws power-law scaling large language models pre-training data augmentations