Scaling Laws for Deepfake Detection

## Abstract

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 for this research, we construct ScaleDF, the largest dataset to date in this field, which contains over 5.8 5.8 million real images from 51 51 different datasets (domains) and more than 8.8 8.8 million fake images generated by 102 102 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.

###### keywords:

## 1 Introduction

The rapid advancement of deepfake technology poses significant challenges to society, ranging from the dissemination of misinformation to the infringement of personal privacy, thereby necessitating the development of effective detection methods. Within the ongoing “arms race" between generation and detection, a central challenge lies in how detection models can generalize to the ever-emerging new forms of forgeries. Increasing the diversity of training data at scale has been considered a promising approach to address this issue. However, this raises a fundamental question: is there a predictable relationship between the improvement of model performance and the growth in data scale?

To address this question, we begin by constructing a dataset of unprecedented diversity and scale, since the scale of existing datasets is insufficient for our research.
Specifically, we introduce ScaleDF, the largest deepfake detection dataset to date, which contains over $5.8$ million real images from $51$ distinct datasets (domains) and more than $8.8$ million fake images generated by $102$ different methods, as shown in Fig. [1](https://arxiv.org/html/2510.16320v1#S1.F1) (a) and (b). Compared to previous datasets, ScaleDF includes approximately $8$ times more real domains and $2$ times more deepfake methods, along with a larger number of images and videos (Fig. [1](https://arxiv.org/html/2510.16320v1#S1.F1) (c)).
In collecting real images, we aim to incorporate all publicly available datasets featuring real faces, covering a wide range of tasks such as face detection, face recognition, and age estimation. When generating fake images, we categorize deepfake generation methods into five types: Face Swapping (FS), Face Reenactment (FR), Full Face synthesis (FF), Face attribute Editing (FE), and Talking Face generation (TF), with $21$, $20$, $24$, $18$, and $19$ methods in each category.
By introducing ScaleDF, the largest and most diverse deepfake detection dataset to date, we enable the discovery of predictable scaling laws, laying out the foundation for building more robust and generalizable deepfake detection systems.
In this work on scaling laws for deepfake detection, we treat the task as a binary classification problem and adopt the Vision Transformer (ViT) (Dosovitskiy et al., 2021) as the backbone. We observe that scaling laws, akin to those found in large language models (LLMs) (Kaplan et al., 2020), emerge when varying the number of training real domains or deepfake methods. Specifically, as shown in Fig. [1](https://arxiv.org/html/2510.16320v1#S1.F1) (d), the detection error exhibits a power-law relationship with respect to the number of real domains or deepfake methods, i.e., $1-\overline{\mathrm{AUC}}=A\cdot N^{-\alpha}$. More importantly, despite the large number of real domains and deepfake methods, we still see no signs of saturation.

Figure: Figure 1: Illustration of ScaleDF, which is the largest deepfake detection dataset across four dimensions: number of real domains, number of deepfake methods, number of videos, and number of images. To ensure a fair comparison, for datasets that do not explicitly provide the number of images, we estimate image counts by sampling one frame per second from the videos. Using this dataset, we present a systematic study on scaling laws for deepfake detection and reveal several insights.
Refer to caption: x1.png

By varying the number of training images, we observe scaling laws similar to those found in image classification (Zhai et al., 2022). Specifically, we observe double-saturating power-law scaling with the format of $1-\overline{\mathrm{AUC}}=c\ +\ K\cdot\left(N+N_{0}\right)^{-\gamma}$. Empirically, with $46$ real domains and $88$ deepfake methods used in training, performance gradually saturates once the number of images exceeds $10^{7}$. However, we expect this saturation threshold to increase if more real domains and deepfake methods are included.
With these observed scaling laws, we aim to transform deepfake-detector development from a heuristic, trial-and-error art into a data-centric engineering discipline.
In addition, we investigate the impact of pre-training and data augmentation in the context of scaling. We compare the performance of the model trained on ScaleDF with that trained on relatively smaller datasets. We also discuss the limitations of scaling itself.

The key contributions of this work are:

- •
We introduce ScaleDF, the largest and most diverse deepfake detection dataset to date. It contains over $5.8$ million real images from $51$ real domains and over $8.8$ million fake images generated by $102$ methods, providing a foundation for scaling law study and future research in this field.
- •
We perform a systematic study on scaling laws for deepfake detection, discovering some predictable relationships between data scale and model performance. Specifically, we observe that the detection error follows a power-law decay as the number of real domains or deepfake methods increases.
- •
We conduct extensive experiments to study the effects of scaling on pre-training and data augmentation, as well as to explore its limitations. With ScaleDF, we also achieve better cross-benchmark generalization over the existing datasets.

## 2 Related Works

Deepfake detection. Deepfake detection aims to distinguish synthesized (or manipulated) faces from real ones. In response to the rapid advancement of face forgery techniques, many effective deepfake detection methods are proposed. For example, DiffusionFake (Chen et al., 2024) leverages diffusion models, while Hitchhikers (Foteinopoulou et al., 2024) utilizes vision-language models (VLMs) to boost detection accuracy. Additionally, several methods focus on artifacts arising from face blending (Zhou et al., 2024b; Sun et al., 2024; Zhou et al., 2024a). Frequency-based approaches also gain attention, with works exploring the use of frequency-domain information (Kashiani et al., 2025; Dutta et al., 2025; Gupta et al., 2025; Tan et al., 2024a). More recently, researchers begin exploring the adaptation and fine-tuning of pre-trained vision-language models for deepfake detection (Yan et al., 2025b; Cui et al., 2025; Yan et al., 2024).
While promising, most of these methods are trained and tested on small datasets and a small number of deepfake generation techniques, which limits their usefulness in real-world scenarios with constantly emerging deepfakes. In contrast, we present a systematic scaling study in deepfake detection, aiming to investigate the underlying scaling laws. Furthermore, we are also aware of the watermark approaches such as SynthID (Kohli, 2025) and Stable Signature (Fernandez et al., 2023) for proactive detection. Our work serves as a complementary approach before these techniques evolve into universal standards.

Scaling laws. Scaling laws play a crucial role in guiding the training of modern foundation models (Li et al., 2025). Since their first introduction for language models (Kaplan et al., 2020; Hestness et al., 2017), numerous studies (Cherti et al., 2023; Aghajanyan et al., 2023; Tay et al., 2022; Hoffmann et al., 2022; Hu et al., 2024; Wei et al., 2022; Hernandez et al., 2021) further validate, extend, and refine scaling laws in the context of large language models (LLMs) and multimodal large language models (MLLMs). Beyond these, scaling laws are also observed in other domains, including autonomous driving (Naumann et al., 2025), image generation (Tian et al., 2024), image classification (Zhai et al., 2022), recommendation systems (Ardalani et al., 2022), and dense retrieval (Fang et al., 2024). This paper aims to extend the study of scaling laws to the domain of deepfake detection. Encouragingly, we observe power-law scaling similar to that shown in LLMs.

## 3 ScaleDF Dataset

Research on scaling laws relies heavily on large and diverse training datasets (Jain et al., 2024; Brill, 2024). However, suitable datasets for this task remain largely absent: the existing datasets either cover only a limited range of deepfake generation methods, or are too small in size. More importantly, the real domains covered in these datasets are also very limited. To solve this, this section introduces the ScaleDF dataset, designed to support research on scaling laws for deepfake detection. Specifically, we first detail the curation process of ScaleDF, and then compare it with existing datasets.

### 3.1 Curating ScaleDF

Real datasets collection. Our guiding principle in collecting real face datasets is inclusiveness, i.e., we aim to incorporate all currently publicly available datasets containing real faces. This enables us to study scaling laws with respect to the number of real datasets (domains). Specifically, we collect datasets spanning a wide range of tasks, including (1) face detection, (2) identity recognition and verification, (3) age and demographic estimation, (4) facial expression, emotion, and valence analysis, (5) audio-visual speech and speaker recognition, (6) masked-face and occlusion-robust detection, (7) multi-spectral and cross-modal biometrics, (8) talking-head synthesis, (9) spatio-temporal action localization, and (10) fairness and bias evaluation.
At the same time, we intentionally exclude most of the datasets that are specifically designed for deepfake detection to prevent overfitting, as the performance of models trained on ScaleDF and evaluated on well-established deepfake detection benchmarks remains trustworthy.
The only exception is made for FaceForensics++ (Rössler et al., 2019), which is included in ScaleDF because of its wide usage in training, and we consider it too valuable a resource to exclude.
Note that we do not include all images or videos from any single dataset, as we consider diversity and balance to be important. For instance, the VGGFace2 dataset (Cao et al., 2018b) contains about $3.31$ million face images, of which we randomly include only $0.2$ million in ScaleDF.
We observe that image overlap and domain similarity between different datasets are inevitable at such a scale. Nevertheless, when splitting the training and testing datasets, we endeavor to perform so-called “cross-domain" evaluation by selecting testing datasets that are rarely covered by recent large-scale face datasets.
The names, sizes, and download links of all included real datasets are provided in the Appendix (Table [6](https://arxiv.org/html/2510.16320v1#A0.T6)).
The statistics of the heights and widths of all real faces in ScaleDF are shown in Fig. [2](https://arxiv.org/html/2510.16320v1#S3.F2).

Figure: Figure 2: Statistics of heights and widths of all real faces in the ScaleDF dataset.
Refer to caption: x2.png

Deepfake generation. Inspired by a recent survey (Pei et al., 2024) and benchmark study (Yan et al., 2024), we categorize deepfake generation methods into five types and provide formal definitions:

- •
Face Swapping (FS): The face of one person is replaced with the face of another.
- •
Face Reenactment (FR): Transfer facial expressions and movements from one person to another.
- •
Full Face synthesis (FF): Generate entirely new faces from scratch (without visual reference).
- •
Face attribute Editing (FE): Modify one person’s specific facial characteristics, such as age, eye, expression, hair, and nose.
- •
Talking Face generation (TF): Synthesize facial movements and lip synchronization from faces.

Similar to the collection of real datasets, we aim to include as many deepfake generation methods as possible to facilitate research on scaling laws along the dimension of deepfake methods. Beyond mere quantity, we also recognize the importance of diversity in two key aspects: (1) Architectural diversity. We cover all major architectures used in deepfake generation, including affine mapping, 3D modeling, variational autoencoders (VAE), generative adversarial networks (GANs), diffusion models, flow matching, and autoregressive (AR) models; (2) Category balance. Across the five categories, we collect $21$, $20$, $24$, $18$, and $19$ methods respectively, ensuring a balanced representation across types.
We observe that similarity between different deepfake methods is inevitable at such a scale. Nevertheless, when splitting training and testing methods, we aim to perform so-called “cross-method" evaluation by selecting testing methods (e.g., GPT-Image-1 (OpenAI, 2025) and SkyReels-A1 (Qiu et al., 2025)) that are not fine-tuned or adapted from any of the training ones.
During generation, we use real datasets for methods that require visual references (FS, FR, FE, TF), while for methods that do not (FF), we use either textual prompts from DiffusionDB (Wang et al., 2023b), JourneyDB (Sun et al., 2023b), and VidProM (Wang and Yang, 2024), or direct noise inputs.
Note that this process uses training real datasets to generate training deepfakes and testing real datasets to generate testing deepfakes, respectively. Therefore, the ScaleDF dataset features both “cross-domain" and “cross-method" evaluation.
For training and testing, we generate about $40,000$ and $2,000$ samples respectively for video-based deepfakes, and about $120,000$ and $6,000$ samples respectively for image-based deepfakes.
The names, categories, architectures, and download links of all included deepfake methods are listed in the Appendix (Table [11](https://arxiv.org/html/2510.16320v1#A9.T11)).

Experimental setup. Although ScaleDF includes both videos and images, we focus on images for training and testing, similar to DF40 (Yan et al., 2024) and DeepfakeBench (Yan et al., 2023), since videos are essentially sequences of images (We acknowledge that temporal artifacts can be helpful for deepfake detection, but this aspect is beyond the scope of this paper.). Specifically, we uniformly sample 3 frames from each generated or real video and use them to represent the video. After that, we follow the standard face detection, alignment, and cropping procedures used in DF40 (Yan et al., 2024) and DeepfakeBench (Yan et al., 2023) to obtain the final processed faces. See Section [A](https://arxiv.org/html/2510.16320v1#A1) and [B](https://arxiv.org/html/2510.16320v1#A2) in the Appendix for more preprocessing details and visualization of processed faces.
Although ScaleDF is large and comprehensive, we remain interested in evaluating the performance of models trained on it when tested “in the wild". This is because, in real-world scenarios, many factors can influence deepfake detection performance: not only novel real domains and deepfake methods, but also image compression, face preprocessing, blending methods, and various perturbations.
To this end, we select six well-established benchmarks published between 2019 and 2024, i.e., DeepFakeDetection (Google, 2019), Celeb-DF V2 (Li et al., 2020), WildDeepFake (Zi et al., 2020), ForgeryNet (He et al., 2021), DeepFakeFace (Song et al., 2023), and DF40 (Yan et al., 2024), to report their direct testing performance. We adopt two commonly used evaluation metrics: the Area Under the Receiver Operating Characteristic Curve (AUC) and the Equal Error Rate (EER).

**Table 1: Compared to existing datasets, ScaleDF includes more real domains, more deepfake methods, and a larger number of videos and images, enabling scaling law research in these dimensions.**
|  | Real | Deepfake | Videos | Images |  |  |
| --- | --- | --- | --- | --- | --- | --- |
| Dataset | Domains | Methods | Real | Fake | Real | Fake |
| DF-TIMIT<br>(Korshunov and Marcel, 2018) | $1$ | $2$ | $320$ | $640$ | - | - |
| UADFV<br>(Yang et al., 2019) | $1$ | $1$ | $49$ | $49$ | - | - |
| FaceForensics++<br>(Rossler et al., 2019) | $1$ | $4$ | $1,000$ | $4,000$ | - | - |
| DeepFakeDetection<br>(Google, 2019) | $1$ | $5$ | $363$ | $3,068$ | - | - |
| Celeb-DF V2<br>(Li et al., 2020) | $1$ | $1$ | $590$ | $5,639$ | - | - |
| WildDeepfake<br>(Zi et al., 2020) | N/A | N/A | $3,805$ | $3,509$ | - | - |
| DFFD<br>(Dang et al., 2020) | $3$ | $8$ | $1,000$ | $3,000$ | $58$K+ | $0.2$M+ |
| DeeperForensics-1.0<br>(Jiang et al., 2020a) | $1$ | $1$ | $50$K | $10$K | - | - |
| DFDC<br>(Dolhansky et al., 2020) | $1$ | $8$ | $23$K+ | $0.1$M+ | - | - |
| ForgeryNet<br>(He et al., 2021) | $4$ | $15$ | $99$K+ | $0.1$M+ | $1.4$M+ | $1.4$M+ |
| FakeAVCeleb<br>(Khalid et al., 2021) | $1$ | $4$ | $500$ | $19.5$K | - | - |
| KoDF<br>(Kwon et al., 2021) | $1$ | $6$ | $62$K+ | $0.1$M+ | - | - |
| FFIW<br>(Zhou et al., 2021) | $1$ | $3$ | $10$K | $10$K | - | - |
| LAV-DF<br>(Cai et al., 2022) | $1$ | $2$ | $36$K+ | $99$K+ | - | - |
| GFW<br>(Borji, 2022) | $2$ | $3$ | - | - | $30$K | $15$K+ |
| DF^3<br>(Ju et al., 2023) | - | $6$ | - | - | - | $46$K+ |
| DeepFakeFace<br>(Song et al., 2023) | $1$ | $3$ | - | - | $30$K | $90$K |
| DF-Platter<br>(Narayan et al., 2023) | $1$ | $3$ | $764$ | $0.1$M+ | - | - |
| DiffusionDeepfake<br>(Chaitali et al., 2024) | - | $2$ | - | - | - | $0.1$M+ |
| AV-Deepfake1M<br>(Cai et al., 2024) | $1$ | $3$ | $0.2$M+ | $0.8$M+ | - | - |
| DiFF<br>(Cheng et al., 2024a) | $2$+ | $13$ | - | - | $23$K+ | $0.5$M+ |
| DF40<br>(Yan et al., 2024) | $6$ | $40$ | $1$K+ | $0.1$M+ | $0.2$M+ | $1$M+ |
| ScaleDF (Ours) | $\mathbf{51}$ | $\mathbf{102}$ | $\mathbf{0.9}$M+ | $\mathbf{1.4}$M+ | $\mathbf{5.8}$M+ | $\mathbf{8.8}$M+ |

### 3.2 Comparing ScaleDF with Similar Datasets

In Table [1](https://arxiv.org/html/2510.16320v1#S3.T1.fig1), we compare the proposed ScaleDF dataset with existing deepfake detection datasets. The reasons they are not suitable for scaling laws research are as follows:

- •
Lack of training real domains. Most of the current deepfake datasets are sourced from only $1$ to $2$ real domains. We observe that only $2$ large-scale datasets cover more than $3$ domains; however, both have their own drawbacks: (1) ForgeryNet (He et al., 2021): although it covers $4$ real domains for training, it includes only $15$ outdated deepfake methods, i.e., none of the latest diffusion or autoregressive models are included; and (2) DF40 (Yan et al., 2024): although it includes $6$ real domains and $40$ deepfake methods, only $1$ real domain (FaceForensics++ (Rossler et al., 2019)) is actually used for training, and most of the fake training images come from that domain. In contrast, ScaleDF covers $51$ real domains, $46$ of which are used for training, enabling research of scaling laws across the real domain dimension.
- •
Insufficient deepfake methods. The dataset that includes the most deepfake methods so far is DF40 (Yan et al., 2024). In comparison, ScaleDF (1) contains twice as many deepfake generation methods, making scaling law research along the method dimension more convincing; (2) incorporates several recent and widely used methods, such as GPT-Image-1 (OpenAI, 2025) and Step1X-Edit (Liu et al., 2025), making the study of scaling laws more practical.
- •
Limited number of images (videos). Given that (1) modern deepfake datasets such as ForgeryNet (He et al., 2021) and DF40 (Yan et al., 2024) contain around $1\sim 2$ million images, and (2) prior work in image classification (Zhai et al., 2022) has observed power-law scaling when increasing dataset size from 1 million to 10 million, we are motivated to explore whether similar scaling laws hold for deepfake detection. To this end, we scale the ScaleDF dataset to over $14$ million images, i.e.more than $5\times$ the size of the previous largest dataset, ForgeryNet (He et al., 2021).

## 4 Scaling Law Observations

In this section, we present the observed scaling laws by training models on the ScaleDF dataset and evaluating them on seven different test datasets. We report the average AUC across these datasets, denoted as $\overline{\mathrm{AUC}}$, here, and present the corresponding scaling laws with average $\mathrm{EER}$ ($\overline{\mathrm{EER}}$) in Appendix (Section [C](https://arxiv.org/html/2510.16320v1#A3)). The complete experimental results can be found in Appendix (Section [E](https://arxiv.org/html/2510.16320v1#A5)).

Training configurations. To eliminate the interference from more advanced deepfake detection methods in our scaling laws research, we formulate the deepfake detection task as a pure binary classification problem. We use the Vision Transformer (ViT) (Dosovitskiy et al., 2021) as the backbone, defaulting to the ViT-Base (Touvron et al., 2022) model pre-trained on ImageNet-21K (Deng et al., 2009). We perform data augmentation by first applying random image quality compression between $40\%$ and $100\%$, followed by randomly selected perturbations from AnyPattern (Wang et al., 2024b). A description of the selected perturbations and other training details are provided in Appendix (Section [D](https://arxiv.org/html/2510.16320v1#A4) and [F](https://arxiv.org/html/2510.16320v1#A6)).

Figure: Figure 3: Left: observed power-law scaling as the number of training real domains changes; Right: observed power-law scaling as the number of training deepfake methods changes. $\mu$ represents the mean computed over $10$ repetitions and $7$ test datasets, while $\sigma$ denotes the variance across the $10$ repetitions.
Refer to caption: x3.png

Power-law scaling is observed with respect to the number of training real domains and deepfake methods, respectively.
To study the scaling law along these dimensions, we randomly sample $N\in\left\{5,8,11,16,23,32\right\}$ real domains from a total of $46$ and $N\in\left\{11,16,22,32,45,64\right\}$ deepfake methods from a total of $88$, respectively. To reduce randomness, each sampling is repeated $10$ times.
For each sampled set of real domains, we train a model using all fake images and the corresponding real images from those domains; while
for each sampled set of deepfake methods, we train a model using all real images and the corresponding fake images generated by those methods. Each $\mu$ in Fig. [3](https://arxiv.org/html/2510.16320v1#S4.F3) represents the mean computed over $10$ repetitions and $7$ test datasets, while $\sigma$ denotes the variance across the $10$ repetitions. Based on these empirical data points, we observe that the trend of $1-\overline{\mathrm{AUC}}$ with respect to the number of real domains or deepfake methods ($N$) is best described by a power law, which is the same form as originally proposed for LLMs (Kaplan et al., 2020), i.e., $1-\overline{\mathrm{AUC}}\ =\ A\cdot N^{-\alpha}$. Using ordinary least squares (OLS), we estimate the parameters as $A=0.729$ with $\alpha=0.476$ for real domains, and $A=0.618$ with $\alpha=0.375$ for deepfake methods. Meanwhile, we calculate the coefficient of determination $R^{2}=0.9821$ and $R^{2}=0.9896$, respectively, implying that the fitted power law explains $98.21\%$ and $98.96\%$ of the variance in the observed data, thus providing an excellent description of the scaling relationship. This scaling law suggests that the performance with respect to the number of real domains and deepfake methods is far from saturated. To achieve higher performance, collecting more real domains and deepfake methods proves to be highly effective. Furthermore, the model’s performance is, to some extent, predictable: for example, to reach an average AUC of $0.95$, we may require about $300$ real domains or $700$ deepfake methods.

The two scaling laws described above are not caused by a change in the number of real or fake images.
When conducting experiments by randomly sampling real domains, the number of real images changes (while the number of fake images remains fixed); conversely, when randomly sampling deepfake methods, the number of fake images changes (with the number of real images unchanged).
A natural question is whether the performance changes or the observed scaling laws are caused by changes in the number of real or fake images. Our answer is no. To support this argument, we conduct two experiments: (1) we keep the number of fake images fixed and randomly sample $1/10$ of the real images for training; the resulting $\overline{\mathrm{AUC}}$ decreases slightly from $0.886$ to $0.879$; (2) we keep the number of real images fixed and randomly sample $1/10$ of the fake images for training; the resulting $\overline{\mathrm{AUC}}$ drops from $0.886$ to $0.876$ slightly. We use $1/10$ here because, when studying scaling laws, we use more than $1/10$ of the real domains or deepfake methods. These small changes in $\overline{\mathrm{AUC}}$ suggest that the observed scaling laws are indeed related to the number of real domains and deepfake methods, rather than the number of real or fake images.

Figure: Figure 4: Left: observed double-saturating power-law scaling as the number of training images changes; $\mu$ represents the mean computed over $5$ repetitions and $7$ test datasets, while $\sigma$ denotes the variance across the $5$ repetitions. Right: performance changes with respect to model sizes.
Refer to caption: x4.png

Double-saturating power-law scaling is observed with respect to the number of training images. To study the scaling law along this dimension, we randomly sample subsets of the ScaleDF training set at the following proportions: ${1/4,1/10,1/16,1/64,1/100,1/256,1/1000}$.
Note that this differs from the previous section, i.e., here we sample real and fake images simultaneously. To reduce randomness, each sampling is repeated $5$ times. Each $\mu$ in Fig. [4](https://arxiv.org/html/2510.16320v1#S4.F4) (left) represents the mean computed over $5$ repetitions and $7$ test datasets, while $\sigma$ denotes the variance across the $5$ repetitions.
Based on these empirical data points, we observe that the trend of $1-\overline{\mathrm{AUC}}$ with respect to the number of training images ($N$) is best described by a double-saturating power law, which is the same form as originally proposed in image classification (Zhai et al., 2022), i.e., $1-\overline{\mathrm{AUC}}=c\ +\ K\cdot\left(N+N_{0}\right)^{-\gamma}$.
Using ordinary least squares (OLS), we estimate the parameters as $c=0.112$, $K=2.1\cdot 10^{4}$, $N_{0}=1.34\cdot 10^{5}$, and $\gamma=0.915$.
Meanwhile, we calculate the coefficient of determination $R^{2}=0.9985$, implying that the fitted power law explains $99.85\%$ of the variance in the observed data, thus providing an excellent description of the scaling relationship.
This scaling law suggests that, with $46$ real domains and $88$ deepfake methods, performance gradually saturates once the total number of training images exceeds $10^{7}$.
To further improve performance, blindly collecting more images from the same real domains or generating more from the same deepfake methods may not be effective.
However, this does not imply that datasets larger than $10^{7}$ images offer no further benefit for deepfake detection. Rather, as we scale up the number of real domains and deepfake methods, more training data will still be essential.

When training on ScaleDF, model size scaling appears to saturate at around $\mathbf{300}$ million parameters. Beyond the aforementioned scaling laws, we are also interested in scaling along the model size dimension, i.e., how large a model ScaleDF can support. Specifically, we select five different model sizes, denoted as ViT-S ($21.7$M), ViT-M ($38.3$M), ViT-B ($85.8$M), ViT-L ($303.4$M), and ViT-H ($630.8$M).
Each $\mu$ in Fig. [4](https://arxiv.org/html/2510.16320v1#S4.F4) (right) represents the mean computed over $7$ test datasets. Performance consistently improves from ViT-S ($21.7$M) to ViT-L ($303.4$M), but saturates thereafter, as further scaling to ViT-H ($630.8$M) yields no gains.
This observation does not suggest that models with more than $300$M parameters bring no additional gain for the deepfake detection task; rather, it indicates the maximum model size currently supported by the ScaleDF dataset. In the future, as we scale up real domains and deepfake methods, larger models may yield better performance.

## 5 Additional Observations with Scaling

Beyond scaling laws, we identify additional insights as deepfake detection is scaled up. We report $\mathrm{AUC}$ results here, with $\mathrm{EER}$ presented in Appendix (Section [G](https://arxiv.org/html/2510.16320v1#A7)). Specifically, we observe that:

**Table 2: Comparison of using different pre-training models: similar performance observed.**
| $\mathrm{AUC}$ | DFD | CDF V2 | Wild | Forgery. | DFF | DF40 | ScaleDF | Mean |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| ImageNet | $0.793$ | $0.915$ | $0.815$ | $0.824$ | $0.909$ | $0.980$ | $0.968$ | $0.886$ |
| CLIP | $0.795$ | $0.894$ | $0.802$ | $0.848$ | $0.954$ | $0.979$ | $0.981$ | $0.893$ |
| SigLIP 2 | $0.750$ | $0.890$ | $0.785$ | $0.855$ | $0.951$ | $0.986$ | $0.983$ | $0.886$ |

**Table 3: Effectiveness of image quality compression (QC) and perturbations (PT).**
| $\mathrm{AUC}$ | DFD | CDF V2 | Wild | Forgery. | DFF | DF40 | ScaleDF | Mean |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| N/A | $0.667$ | $0.805$ | $0.781$ | $0.756$ | $0.808$ | $0.937$ | $0.975$ | $0.818$ |
| QC | $0.774$ | $0.914$ | $0.802$ | $0.770$ | $0.932$ | $0.978$ | $0.976$ | $0.878$ |
| QC + PT | $0.793$ | $0.915$ | $0.815$ | $0.824$ | $0.909$ | $0.980$ | $0.968$ | $0.886$ |

Different pre-trained models exhibit similar performance with the ScaleDF. Several works (Ojha et al., 2023; Yan et al., 2024, 2025a) have reported that, on small-scale datasets, fine-tuning pre-trained vision-language models yields better performance than fine-tuning pre-trained image classification models. Here, we aim to examine whether this claim holds when training on the large-scale ScaleDF dataset, which contains over $14$ million images. Our findings indicate that the answer is no. Specifically, we compare three types of pre-trained ViT-Base models: (1) image classification on ImageNet-21K, (2) CLIP (Radford et al., 2021), and (3) SigLIP 2 (Tschannen et al., 2025). Experiments in Table [2](https://arxiv.org/html/2510.16320v1#S5.T2.fig1) show that there are no significant performance differences (less than $1\%$ in $\mathrm{AUC}$) across different pre-training methods. Nevertheless, we also observe that without pre-training, convergence is very slow, and thus pre-training remains necessary for the ScaleDF.

Data augmentation remains important in the context of scaling. In this section, we investigate the importance of data augmentation in training on the large-scale ScaleDF dataset. A common understanding is that data augmentation serves as a remedy for data scarcity, especially in scenarios where models are data-hungry. For example, DeiT (Touvron et al., 2021) demonstrates that, with appropriate data augmentation, it is possible to achieve competitive performance using only ImageNet-1k, compared to models pre-trained on hundreds of millions of images. However, we observe that for the deepfake detection task, data augmentation remains important even when sufficient training data is available. From Table [3](https://arxiv.org/html/2510.16320v1#S5.T3.fig1), we observe that: (1) random image quality compression can significantly improve performance, for example, increasing the $\mathrm{AUC}$ on Celeb-DF V2 (Li et al., 2020) from $0.805$ to $0.914$; and (2) random perturbations enhance robustness on test sets with strong perturbations, such as ForgeryNet (He et al., 2021). These improvements also suggest that deepfake detection relies, to some extent, on low-level features.

**Table 4: Comparison in cross-benchmark setting: with scaling, we achieve the best performance.**
| Training sets | $\mathrm{AUC}$ | DFD | CDF V2 | Wild | Forgery. | DFF | DF40 | ScaleDF |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
|  | DFD | $-$ | $0.802$ | $0.738$ | $0.610$ | $0.545$ | $0.594$ | $0.524$ |
|  | CDF V2 | $0.709$ | $-$ | $0.777$ | $0.640$ | $0.599$ | $0.665$ | $0.612$ |
|  | Wild | $0.643$ | $0.757$ | $-$ | $0.555$ | $0.489$ | $0.562$ | $0.556$ |
|  | Forgery. | $0.813$ | $0.913$ | $0.821$ | $-$ | $0.687$ | $0.833$ | $0.657$ |
|  | DFF | $0.583$ | $0.568$ | $0.552$ | $0.578$ | $-$ | $0.669$ | $0.622$ |
|  | DF40 | $0.587$ | $0.800$ | $0.684$ | $0.677$ | $0.607$ | $-$ | $0.667$ |
|  | ScaleDF | $0.793$ | $0.915$ | $0.815$ | $0.824$ | $0.909$ | $0.980$ | $-$ |

**Table 5: Comparison of whether includes FaceForensics++ (Rössler et al., 2019) in the ScaleDF.**
| $\mathrm{AUC}$ | DFD | CDF V2 | Wild | Forgery. | DFF | DF40 | ScaleDF | Mean |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| w/o FF++ | $0.758$ | $0.868$ | $0.796$ | $0.807$ | $0.905$ | $0.978$ | $0.969$ | $0.869$ |
| w FF++ | $0.793$ | $0.915$ | $0.815$ | $0.824$ | $0.909$ | $0.980$ | $0.968$ | $0.886$ |

Scaling proves essential for achieving satisfactory cross-benchmark performance. Unlike previous dataset papers, such as ForgeryNet (He et al., 2021), DF40 (Yan et al., 2024), and DiFF (Cheng et al., 2024a), which mainly focus on proposing new comprehensive benchmarks and conducting extensive evaluations, we emphasize cross-benchmark performance, i.e., training models on ScaleDF and testing them on other well-established benchmarks. This better reflects whether researchers and engineers can train a model on the proposed dataset and directly deploy it in real-world production environments. In Table [4](https://arxiv.org/html/2510.16320v1#S5.T4.fig1), we compare the cross-benchmark capability of the proposed ScaleDF with that of existing datasets. It is observed that:
(1) Although recent datasets such as DF40 (Yan et al., 2024) are large-scale and cover many deepfake methods, they still do not perform well in cross-benchmark testing. We infer that this is due to insufficient coverage of real domains.
(2) ForgeryNet (He et al., 2021) does not achieve consistently good performance across all datasets, likely because it lacks coverage of some recent deepfake generation methods.
(3) As expected, small-scale datasets do not perform well on cross-benchmark settings, as they cover neither a wide range of real domains nor diverse deepfake methods.

Scaling is important but not all you need, and methodological innovations are still necessary.
As an ablation study, we remove FaceForensics++ (Rössler et al., 2019) from ScaleDF, i.e., reducing one real domain and four deepfake methods, and train a new model on the modified dataset. From Table [5](https://arxiv.org/html/2510.16320v1#S5.T5.fig1), we observe a performance drop on older benchmarks, while the performance on newer benchmarks remains unchanged.
This implies that simply adding more new deepfake methods does not significantly improve performance on fundamentally different, older ones. To enhance performance on such benchmarks, it is necessary to include deepfake methods similar to those used in older datasets (e.g., those found in FaceForensics++). This further implies that, even though ScaleDF includes a wide range of deepfake methods, models trained with simple binary classification may not generalize well to totally different ones that could appear in the future. This suggests the need for algorithms that (1) can learn the underlying essence of forgery from a large number of deepfake methods, and (2) generalize far beyond what simple binary classification allows. We hope that the ScaleDF dataset provides sufficient resources to explore and develop such algorithms, and we call for efforts from the community.

## 6 Conclusion

In this paper, we introduce ScaleDF, the most comprehensive deepfake detection dataset to date, spanning $51$ real domains, $102$ deepfake methods, and more than $14$ million face images.
Leveraging this unprecedented scale, we conduct a systematic study of scaling laws in deepfake detection.
Our primary finding is that detection performance follows predictable power-law scaling relationships. Specifically, we show that detection error decreases as a power-law function of the number of training real domains or deepfake methods, with no signs of saturation. This discovery shifts the development of deepfake detectors from a heuristic-driven process to a more predictable, data-centric engineering discipline.
We further identify a double-saturating power law with respect to the number of training images, suggesting that when the diversity of sources is fixed, the benefit of simply increasing data quantity eventually diminishes.
Strategically, we should focus on the diversity and comprehensiveness of the evolving deepfake methods when building such a database.
In addition, our large-scale experiments yield several practical insights: data augmentation remains crucial for robustness, even with massive datasets, and models trained on ScaleDF achieve better cross-benchmark generalization.
However, our work also highlights that scaling alone is not a panacea; generalizing to fundamentally novel or unseen forgery types remains a challenge, underscoring the need for innovation in detection algorithms.
By building ScaleDF, we aim to provide a resource for the research community to explore these frontiers and encourage future efforts to develop algorithms capable of learning more fundamental forgery representations from a large and diverse dataset.

Disclaimer for scaling law research.
We emphasize that the scaling laws presented in this work are empirical observations specific to our experimental setup. In the study of scaling laws, fitted parameters and empirical findings are known to vary depending on choices of hyperparameter configurations, optimization methods, model architectures, data quality, and other experimental conditions (Cherti et al., 2023; Bahri et al., 2024). The specific exponents and constants we report are contingent on our use of the ViT architecture, the composition of the ScaleDF dataset, and our chosen training and data augmentation protocols. As such, these laws should be interpreted as descriptive models for deepfake detection scaling within this context, rather than as universal, fundamental constants. The primary value of our findings is the demonstration that predictable scaling is achievable in this domain, providing a data-centric framework for future development.
Future work exploring different architectures or data compositions would likely yield different scaling coefficients.

## Ethics Statement

This work aims to advance deepfake detection and foster Generative AI safety. We adhere to the license terms of all data and models in constructing ScaleDF. There are a few open issues and important considerations around fairness and bias. Please refer to Appendix (Section [H](https://arxiv.org/html/2510.16320v1#A8)) for a detailed discussion.

## Reproducibility Statement

A detailed description of the dataset curation process is provided in Section [3](https://arxiv.org/html/2510.16320v1#S3), along with complete lists and links to all source real datasets (Table [6](https://arxiv.org/html/2510.16320v1#A0.T6)) and deepfake generation methods (Table [11](https://arxiv.org/html/2510.16320v1#A9.T11)).
The data preprocessing pipeline, which includes face detection, alignment, and cropping, is described in Appendix (Section [A](https://arxiv.org/html/2510.16320v1#A1)).
All experimental settings, such as model configurations, data augmentation strategies, training hyperparameters, and computational infrastructure, are documented in Section [4](https://arxiv.org/html/2510.16320v1#S4) (Training Configurations) and Appendix (Section [D](https://arxiv.org/html/2510.16320v1#A4) and [F](https://arxiv.org/html/2510.16320v1#A6)).
To support verification of our scaling law findings, we provide complete numerical results for all experiments, including Equal Error Rate (EER) metrics, in Appendix (Section [C](https://arxiv.org/html/2510.16320v1#A3) and [E](https://arxiv.org/html/2510.16320v1#A5)).

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## Appendix A Data Preprocessing Pipeline

In this section, we describe the data preprocessing pipeline, which we follow from DF40 (Yan et al., 2024) and DeepfakeBench (Yan et al., 2023), consisting of face detection, alignment, and cropping.

Face detection.
For each input image, the preprocessing begins with locating the facial region. We utilize the frontal face detector from the Dlib library (King, 2009), a widely adopted method based on Histogram of Oriented Gradients (HOG) features. The detector identifies all potential facial bounding boxes in the image. To focus on the primary subject, when multiple faces are detected, we select the one with the largest bounding box area. If no face is detected, the image is discarded.

Alignment.
Following face detection, a facial alignment procedure is applied to standardize the pose and scale of the detected faces. This process involves two key steps:
(1) Landmark localization. We employ Dlib’s pre-trained 81-point facial landmark predictor to accurately identify key facial features. From the detected landmarks, we select five critical points used for alignment: the centers of the left and right eyes, the tip of the nose, and the left and right corners of the mouth.
(2) Transformation estimation. A similarity transformation is computed to map the selected landmarks to a predefined set of canonical coordinates that represent an ideal, upright facial configuration.

Cropping.
The final step leverages the alignment information to crop the face from the original image. The computed affine transformation matrix is applied to the full image, simultaneously rotating, scaling, and translating it to center the aligned face within a new canvas. A scaling parameter, set to $1.3$ in our implementation, defines the cropping boundary to avoid overly tight crops and preserve contextual facial regions such as the forehead, chin, and hair. The resulting aligned and cropped image is then resized to a fixed resolution of $256\times 256$ pixels for subsequent processing.

## Appendix B Visualization of processed faces

As illustrated in Tables [20](https://arxiv.org/html/2510.16320v1#A9.T20) to [35](https://arxiv.org/html/2510.16320v1#A9.T35), five processed faces are randomly sampled for each real dataset (domain) and deepfake method to provide visual examples.

## Appendix C Scaling laws with EER ¯ \overline{\mathrm{EER}}

Figure: Figure 5: Left Top: observed power-law scaling as the number of training real domains changes; Right Top: observed power-law scaling as the number of training deepfake methods changes; Left Bottom: observed double-saturating power-law scaling as the number of training images changes; Right Bottom: performance changes with respect to model sizes. $\mu$ represents the mean computed over repetitions and test datasets, while $\sigma$ denotes the variance across repetitions.
Refer to caption: x5.png

In this section, we investigate whether similar scaling laws hold under a different evaluation metric, i.e., $\mathrm{EER}$. To this end, we replicate the scaling law analysis presented in the main paper.
As shown in Fig. [5](https://arxiv.org/html/2510.16320v1#A3.F5), similar scaling laws can also be observed in terms of $\overline{\mathrm{EER}}$ (The complete experimental results can be found in Appendix (Section [E](https://arxiv.org/html/2510.16320v1#A5))). Specifically, we conclude that:

Power-law scaling is observed with respect to the number of training real domains and deepfake methods, respectively.
To study the scaling law along these dimensions, we randomly sample $N\in\left\{5,8,11,16,23,32\right\}$ real domains from a total of $46$ and $N\in\left\{11,16,22,32,45,64\right\}$ deepfake methods from a total of $88$, respectively. To reduce randomness, each sampling is repeated $10$ times.
For each sampled set of real domains, we train a model using all fake images and the corresponding real images from those domains; while
for each sampled set of deepfake methods, we train a model using all real images and the corresponding fake images generated by those methods. Each $\mu$ in Fig. [5](https://arxiv.org/html/2510.16320v1#A3.F5) (Top) represents the mean computed over $10$ repetitions and $7$ test datasets, while $\sigma$ denotes the variance across the $10$ repetitions. Based on these empirical data points, we observe that the trend of $\overline{\mathrm{EER}}$ with respect to the number of real domains or deepfake methods ($N$) is best described by a power law, which is the same form as originally proposed for LLMs (Kaplan et al., 2020), i.e., $\overline{\mathrm{EER}}\ =\ A\cdot N^{-\alpha}$. Using ordinary least squares (OLS), we estimate the parameters as $A=0.627$ with $\alpha=0.316$ for real domains, and $A=0.552$ with $\alpha=0.244$ for deepfake methods. Meanwhile, we calculate the coefficient of determination $R^{2}=0.9874$ and $R^{2}=0.9928$, respectively, implying that the fitted power law explains $98.74\%$ and $99.28\%$ of the variance in the observed data, thus providing an excellent description of the scaling relationship. This scaling law suggests that the performance with respect to the number of real domains and deepfake methods is far from saturated. To achieve higher performance, collecting more real domains and deepfake methods proves to be highly effective. Furthermore, the model’s performance is, to some extent, predictable: for example, to reach an average EER of $0.10$, we may require about $340$ real domains or $1100$ deepfake methods.

The two scaling laws described above are not caused by a change in the number of real or fake images.
When conducting experiments by randomly sampling real domains, the number of real images changes (while the number of fake images remains fixed); conversely, when randomly sampling deepfake methods, the number of fake images changes (with the number of real images unchanged).
A natural question is whether the performance changes or the observed scaling laws are caused by changes in the number of real or fake images. Our answer is no. To support this argument, we conduct two experiments: (1) we keep the number of fake images fixed and randomly sample $1/10$ of the real images for training; the resulting $\overline{\mathrm{EER}}$ decreases slightly from $0.184$ to $0.193$; (2) we keep the number of real images fixed and randomly sample $1/10$ of the fake images for training; the resulting $\overline{\mathrm{EER}}$ drops from $0.184$ to $0.198$ slightly. We use $1/10$ here because, when studying scaling laws, we use more than $1/10$ of the real domains or deepfake methods. These small changes in $\overline{\mathrm{EER}}$ suggest that the observed scaling laws are indeed related to the number of real domains and deepfake methods, rather than the number of real or fake images.

Double-saturating power-law scaling is observed with respect to the number of training images. To study the scaling law along this dimension, we randomly sample subsets of the ScaleDF training set at the following proportions: ${1/4,1/10,1/16,1/64,1/100,1/256,1/1000}$.
Note that this differs from the previous section, i.e., here we sample real and fake images simultaneously. To reduce randomness, each sampling is repeated $5$ times. Each $\mu$ in Fig. [5](https://arxiv.org/html/2510.16320v1#A3.F5) (Left Bottom) represents the mean computed over $5$ repetitions and $7$ test datasets, while $\sigma$ denotes the variance across the $5$ repetitions.
Based on these empirical data points, we observe that the trend of $\overline{\mathrm{EER}}$ with respect to the number of training images ($N$) is best described by a double-saturating power law, which is the same form as originally proposed in image classification (Zhai et al., 2022), i.e., $\overline{\mathrm{EER}}=c\ +\ K\cdot\left(N+N_{0}\right)^{-\gamma}$.
Using ordinary least squares (OLS), we estimate the parameters as $c=0.175$, $K=1.44\cdot 10^{3}$, $N_{0}=1.19\cdot 10^{5}$, and $\gamma=0.710$.
Meanwhile, we calculate the coefficient of determination $R^{2}=0.9989$, implying that the fitted power law explains $99.89\%$ of the variance in the observed data, thus providing an excellent description of the scaling relationship.
This scaling law suggests that, with $46$ real domains and $88$ deepfake methods, performance gradually saturates once the total number of training images exceeds $10^{7}$.
To further improve performance, blindly collecting more images from the same real domains or generating more from the same deepfake methods may not be effective.
However, this does not imply that datasets larger than $10^{7}$ images offer no further benefit for deepfake detection. Rather, as we scale up the number of real domains and deepfake methods, more training data will still be essential.

When training on ScaleDF, model size scaling appears to saturate at around $\mathbf{300}$ million parameters. Beyond the aforementioned scaling laws, we are also interested in scaling along the model size dimension, i.e., how large a model ScaleDF can support. Specifically, we select five different model sizes, denoted as ViT-S ($21.7$M), ViT-M ($38.3$M), ViT-B ($85.8$M), ViT-L ($303.4$M), and ViT-H ($630.8$M).
Each $\mu$ in Fig. [5](https://arxiv.org/html/2510.16320v1#A3.F5) (Right Bottom) represents the mean computed over $7$ test datasets. Performance consistently improves from ViT-S ($21.7$M) to ViT-L ($303.4$M), but saturates thereafter, as further scaling to ViT-H ($630.8$M) yields no gains.
This observation does not suggest that models with more than $300$M parameters bring no additional gain for the deepfake detection task; rather, it indicates the maximum model size currently supported by the ScaleDF dataset. In the future, as we scale up real domains and deepfake methods, larger models may yield better performance.

## Appendix D Used perturbations

Figure: Figure 6: Used original face.
Refer to caption: original_face.jpeg

In this section, we describe how the perturbations from AnyPattern (Wang et al., 2024b) are utilized. AnyPattern is a large-scale perturbation dataset containing 100 perturbations. The code for generating each perturbation is available at [here](https://github.com/WangWenhao0716/AnyPattern/blob/main/Generate/code_for_all_patterns.py). However, since not all of them occur frequently in real-world scenarios, we select $30$ common perturbations for training. To demonstrate the effect of the 30 selected perturbations, Fig. [6](https://arxiv.org/html/2510.16320v1#A4.F6) shows the original face, while Tables [36](https://arxiv.org/html/2510.16320v1#A9.T36), [37](https://arxiv.org/html/2510.16320v1#A9.T37), and [38](https://arxiv.org/html/2510.16320v1#A9.T38) present example faces with the applied perturbations. During training, for each image, we apply no perturbations with a probability of $50\%$, a single perturbation with a probability of $25\%$, and two perturbations with a probability of $25\%$.

**Table 7: Comparison of using different pre-training models: similar performance observed.**
| $\mathrm{EER}$ | DFD | CDF V2 | Wild | Forgery. | DFF | DF40 | ScaleDF | Mean |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| ImageNet | $0.281$ | $0.162$ | $0.268$ | $0.260$ | $0.161$ | $0.063$ | $0.093$ | $0.184$ |
| CLIP | $0.278$ | $0.179$ | $0.279$ | $0.231$ | $0.085$ | $0.065$ | $0.052$ | $0.167$ |
| SigLIP 2 | $0.316$ | $0.193$ | $0.290$ | $0.229$ | $0.094$ | $0.052$ | $0.052$ | $0.175$ |

## Appendix E Complete experimental results for scaling laws

In the main paper, we skip the exact values for each data point used in the scaling law analysis. To improve the reproducibility, we present all experimental results here in full detail. Specifically:
(1) Tables [12](https://arxiv.org/html/2510.16320v1#A9.T12.fig1) and [16](https://arxiv.org/html/2510.16320v1#A9.T16.fig1) report the exact $\mathrm{AUC}$ and $\mathrm{EER}$ values for scaling laws with respect to the number of real domains;
(2) Tables [13](https://arxiv.org/html/2510.16320v1#A9.T13.fig1) and [17](https://arxiv.org/html/2510.16320v1#A9.T17.fig1) report the exact $\mathrm{AUC}$ and $\mathrm{EER}$ values with respect to the number of deepfake methods;
(3) Tables [15](https://arxiv.org/html/2510.16320v1#A9.T15) and [19](https://arxiv.org/html/2510.16320v1#A9.T19) report the exact $\mathrm{AUC}$ and $\mathrm{EER}$ values with respect to the number of training images; and
(4) Tables [15](https://arxiv.org/html/2510.16320v1#A9.T15) and [19](https://arxiv.org/html/2510.16320v1#A9.T19) report the exact $\mathrm{AUC}$ and $\mathrm{EER}$ values with respect to different model sizes.

## Appendix F Training details

We report training details here, basically following the scaling law reproducibility checklist (Li et al., 2025). The generation of ScaleDF required about $20,000$ A100 GPU hours. Training is distributed across $8$ NVIDIA A100 40GB NVLink GPUs and $128$ AMD EPYC 7742 CPU cores. Each training run requires approximately $160$ GPU hours. Before training, all images are resized to a resolution of $224\times 224$ pixels. We use a batch size of $2,048$ and train for $20$ epochs with class balancing.
The AdamW optimizer (Loshchilov and Hutter, 2017) is used with a cosine-decay learning rate schedule, a peak learning rate of $10^{-5}$, and $5$ warm-up epochs.

## Appendix G Additional Observations in EER \mathrm{EER}

As shown in Tables [10](https://arxiv.org/html/2510.16320v1#A4.T10.fig1) to [10](https://arxiv.org/html/2510.16320v1#A4.T10.fig1), the observations in Section [5](https://arxiv.org/html/2510.16320v1#S5) still hold when evaluated with an alternative metric.

Figure: Figure 7: Perceived demographic distribution of faces in ScaleDF.
Refer to caption: x6.png

## Appendix H Ethics Statement

We adhere to the license terms of all data and models in constructing ScaleDF. We are committed to responsible research, though there are open issues around fairness and bias. We put some important considerations here for interested readers.

### H.1 Licenses

Our work builds upon numerous publicly available resources. In constructing ScaleDF, we have made every effort to comply with the licenses of all constituent datasets and deepfake generation methods. Full lists of these resources, along with direct links to their original sources, are provided in Table [6](https://arxiv.org/html/2510.16320v1#A0.T6) and Table [11](https://arxiv.org/html/2510.16320v1#A9.T11) to ensure transparency and enable reproducibility.

### H.2 Fairness

Recognizing that large-scale datasets can inherit and amplify societal biases, we have analyzed the demographic distribution of what the faces appear to be within ScaleDF, using pre-trained models from third parties. While we aimed for inclusiveness, as shown in Fig. [7](https://arxiv.org/html/2510.16320v1#A7.F7), our dataset inherits imbalances from the included real domains and deepfake methods, which we document here to ensure transparency and guide future research.

- •
Perceived Monk Skin Tone (MST) Distribution. The dataset shows an imbalance across different MSTs, with a strong concentration around MSTs 5, 6, 7, 8 (out of 10). This over-representation may result in performance disparities in detection models.
- •
Perceived Age Distribution. There is a strong concentration in the $20\sim 29$ (about $7.5$ million) and $30\sim 39$ (about $3.5$ million) perceived age groups. Younger and older individuals are under-represented, with groups like $>70$ and $0\sim 2$ containing fewer than $0.1$ million faces each. This may limit the reliability of detectors for very young or elderly subjects.
- •
Perceived Gender Distribution. The perceived gender distribution is more balanced compared with age, but still shows a skew. The dataset includes about $8.8$ million male faces and $5.8$ million female faces.

We acknowledge these demographic biases as a limitation of ScaleDF. This is the nature of the best-effort data acquisition process from the public domain. We encourage future researchers to apply sampling techniques to promote a better fairness outcome.

### H.3 Broader Impact

The primary goal of this research is to advance the capabilities of deepfake detection to combat malicious activities such as the spread of misinformation and the violation of personal privacy. By establishing predictable scaling laws, we provide the research community with valuable knowledge to build more robust and generalizable detectors, thereby strengthening societal defenses against manipulated media.

## Appendix I LLM Usage

We use an LLM-based writing assistant for minor grammar and style edits. All technical content, analyses, and conclusions are authored and verified by the human authors.

**Table 11: Deepfake methods included in the ScaleDF, with the testing ones highlighted in .**
| No. | Method | Cat. | Arch. | Link |
| --- | --- | --- | --- | --- |
| 0 | Faceswap (Earl, 2015) | FS | Affine | [Code](https://github.com/matthewearl/faceswap) |
| $1$ | FaceSwap (Kowalski, 2016) | FS | 3D | [Code](https://github.com/MarekKowalski/FaceSwap/) |
| $2$ | DeepFakes (deepfakes, 2017) | FS | VAE | [Code](https://github.com/deepfakes/faceswap) |
| $3$ | FSGAN (Nirkin et al., 2019) | FS | GAN | [Code](https://github.com/YuvalNirkin/fsgan) |
| $4$ | SimSwap (Chen et al., 2020) | FS | GAN | [Code](https://github.com/neuralchen/SimSwap) |
| $5$ | HifiFace (Wang et al., 2021c) | FS | 3D | [Code](https://github.com/maum-ai/hififace) |
| $6$ | InfoSwap (Gao et al., 2021) | FS | GAN | [Code](https://github.com/GGGHSL/InfoSwap-master) |
| $7$ | UniFace (Xu et al., 2022a) | FS | GAN | [Code](https://github.com/xc-csc101/UniFace) |
| $8$ | MobileFaceSwap (Xu et al., 2022b) | FS | GAN | [Code](https://github.com/Seanseattle/MobileFaceSwap) |
| $9$ | E4S (Liu et al., 2022) | FS | GAN | [Code](https://github.com/e4s2022/e4s) |
| $10$ | GHOST (Groshev et al., 2022) | FS | GAN | [Code](https://github.com/ai-forever/ghost) |
| $11$ | BlendFace (Shiohara et al., 2023) | FS | GAN | [Code](https://github.com/mapooon/BlendFace) |
| $12$ | FaceDancer (Rosberg et al., 2023) | FS | GAN | [Code](https://github.com/felixrosberg/FaceDancer) |
| $13$ | 3DSwap (Li et al., 2023) | FS | 3D | [Code](https://github.com/VISION-SJTU/3dSwap) |
| $14$ | Inswapper (Wang, 2023) | FS | GAN | [Code](https://github.com/haofanwang/inswapper) |
| $15$ | FaceAdapter (Han et al., 2024) | FS | Diff. | [Code](https://github.com/FaceAdapter/Face-Adapter) |
| $16$ | CSCS (Huang et al., 2024) | FS | GAN | [Code](https://github.com/ICTMCG/CSCS) |
| $17$ | REFace (Baliah et al., 2024) | FS | Diff. | [Code](https://github.com/Sanoojan/REFace) |
| $18$ | FaceFusion (Wang, 2024) | FS | GAN | [Code](https://github.com/idootop/TinyFace) |
| $19$ | InstantID (Wang et al., 2024a) | FS | Diff. | [Code](https://github.com/nosiu/InstantID-faceswap) |
| $20$ | DiffFace (Kim et al., 2025) | FS | Diff. | [Code](https://github.com/hxngiee/DiffFace) |
| $21$ | Face2Face (Thies et al., 2016) | FR | 3D | [Code](https://justusthies.github.io/posts/acm-research-highlight/) |
| $22$ | FOMM (Siarohin et al., 2019) | FR | Affine | [Code](https://github.com/AliaksandrSiarohin/first-order-model) |
| $23$ | NeuralTextures (Thies et al., 2019) | FR | 3D | [Code](https://github.com/SSRSGJYD/NeuralTexture) |
| $24$ | OneShot (Wang et al., 2021b) | FR | 3D | [Code](https://github.com/zhanglonghao1992/One-Shot_Free-View_Neural_Talking_Head_Synthesis/) |
| $25$ | Face-Vid2Vid (Zheng, 2021) | FR | 3D | [Code](https://github.com/zhengkw18/face-Vid2Vid) |
| $26$ | TPSMM (Zhao et al., 2022) | FR | Affine | [Code](https://github.com/yoyo-nb/Thin-Plate-Spline-Motion-Model) |
| $27$ | DaGAN (Hong et al., 2022) | FR | GAN | [Code](https://github.com/harlanhong/CVPR2022-DaGAN) |
| $28$ | LIA (Wang et al., 2022) | FR | Affine | [Code](https://github.com/wyhsirius/LIA) |
| $29$ | AMatrix (Bounareli et al., 2022) | FR | GAN | [Code](https://github.com/StelaBou/stylegan_directions_face_reenactment) |
| $30$ | StyleMask (Bounareli et al., 2023a) | FR | GAN | [Code](https://github.com/StelaBou/StyleMask) |
| $31$ | MRFA (Tao et al., 2023) | FR | Affine | [Code](https://github.com/JialeTao/MRFA) |
| $32$ | HyperReenact (Bounareli et al., 2023b) | FR | GAN | [Code](https://github.com/StelaBou/HyperReenact) |
| $33$ | MCNet (Hong and Xu, 2023) | FR | Affine | [Code](https://github.com/harlanhong/ICCV2023-MCNET) |
| $34$ | CVTHead (Ma et al., 2024a) | FR | 3D | [Code](https://github.com/HowieMa/CVTHead) |
| $35$ | FollowYourEmoji (Ma et al., 2024b) | FR | Diff. | [Code](https://github.com/mayuelala/FollowYourEmoji) |
| $36$ | LivePortrait (Guo et al., 2024) | FR | 3D | [Code](https://github.com/KwaiVGI/LivePortrait) |
| $37$ | Megactor (Yang et al., 2024) | FR | Diff. | [Code](https://github.com/megvii-research/megactor) |
| $38$ | G3FA (Javanmardi et al., 2024) | FR | 3D | [Code](https://github.com/dfki-av/G3FA/tree/main) |
| $39$ | FSRT (Rochow et al., 2024) | FR | Affine | [Code](https://github.com/andrerochow/fsrt) |
| $40$ | SkyReels-A1 (Qiu et al., 2025) | FR | Diff. | [Code](https://github.com/SkyworkAI/SkyReels-A1) |
| $41$ | StyleGAN2 (Karras et al., 2020) | FF | GAN | [Code](https://github.com/NVlabs/stylegan2) |
| $42$ | VQGAN (Esser et al., 2021) | FF | GAN | [Code](https://github.com/CompVis/taming-transformers/) |
| $43$ | StyleGAN3 (Karras et al., 2021) | FF | GAN | [Code](https://github.com/NVlabs/stylegan3) |
| $44$ | StyleGAN-XL (Sauer et al., 2022) | FF | GAN | [Code](https://github.com/autonomousvision/stylegan-xl) |
| $45$ | SD2.1 (Rombach et al., 2022) | FF | Diff. | [Code](https://huggingface.co/stabilityai/stable-diffusion-2-1) |
| $46$ | SD1.5 (Rombach et al., 2022) | FF | Diff. | [Code](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) |
| $47$ | SDXL (Podell et al., 2023) | FF | Diff. | [Code](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) |
| $48$ | PixArt-Alpha (PixArt, 2023) | FF | Diff. | [Code](https://huggingface.co/PixArt-alpha/PixArt-XL-2-512x512) |
| $49$ | Midjourney (Sun et al., 2023a) | FF | N/A | [Source](https://huggingface.co/datasets/JourneyDB/JourneyDB) |
| $50$ | SD3.5 (Esser et al., 2024) | FF | Diff. | [Code](https://huggingface.co/stabilityai/stable-diffusion-3.5-large) |

**Table 12: Full experimental results measured by $\mathrm{AUC}$ for scaling law with varying numbers of real domains. The concluded scaling law is shown in Fig. [3](https://arxiv.org/html/2510.16320v1#S4.F3) (Left).**
| $\mathrm{AUC}$ | DFD | CDF V2 | Wild | Forgery. | DFF | DF40 | ScaleDF | Mean |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| $46$ | $0.793$ | $0.915$ | $0.815$ | $0.824$ | $0.909$ | $0.980$ | $0.968$ | $0.886$ |
| $32$ | $0.752$ | $0.845$ | $0.716$ | $0.755$ | $0.881$ | $0.927$ | $0.966$ | $0.835$ |
| $32$ | $0.764$ | $0.917$ | $0.747$ | $0.818$ | $0.907$ | $0.985$ | $0.968$ | $0.872$ |
| $32$ | $0.779$ | $0.904$ | $0.727$ | $0.817$ | $0.920$ | $0.981$ | $0.967$ | $0.871$ |
| $32$ | $0.765$ | $0.897$ | $0.817$ | $0.817$ | $0.865$ | $0.977$ | $0.960$ | $0.871$ |
| $32$ | $0.760$ | $0.904$ | $0.732$ | $0.811$ | $0.863$ | $0.980$ | $0.960$ | $0.859$ |
| $32$ | $0.780$ | $0.912$ | $0.728$ | $0.799$ | $0.913$ | $0.977$ | $0.963$ | $0.868$ |
| $32$ | $0.710$ | $0.847$ | $0.804$ | $0.726$ | $0.840$ | $0.816$ | $0.911$ | $0.808$ |
| $32$ | $0.768$ | $0.903$ | $0.829$ | $0.790$ | $0.903$ | $0.972$ | $0.956$ | $0.874$ |
| $32$ | $0.737$ | $0.834$ | $0.805$ | $0.786$ | $0.876$ | $0.919$ | $0.963$ | $0.846$ |
| $32$ | $0.762$ | $0.898$ | $0.813$ | $0.781$ | $0.906$ | $0.937$ | $0.963$ | $0.866$ |
| $23$ | $0.748$ | $0.890$ | $0.778$ | $0.808$ | $0.843$ | $0.946$ | $0.935$ | $0.850$ |
| $23$ | $0.723$ | $0.824$ | $0.793$ | $0.727$ | $0.877$ | $0.890$ | $0.933$ | $0.824$ |
| $23$ | $0.760$ | $0.883$ | $0.753$ | $0.761$ | $0.863$ | $0.944$ | $0.960$ | $0.846$ |
| $23$ | $0.742$ | $0.905$ | $0.707$ | $0.762$ | $0.864$ | $0.976$ | $0.942$ | $0.843$ |
| $23$ | $0.713$ | $0.852$ | $0.760$ | $0.752$ | $0.803$ | $0.936$ | $0.944$ | $0.823$ |
| $23$ | $0.766$ | $0.889$ | $0.780$ | $0.782$ | $0.828$ | $0.972$ | $0.950$ | $0.852$ |
| $23$ | $0.787$ | $0.886$ | $0.794$ | $0.758$ | $0.895$ | $0.967$ | $0.961$ | $0.864$ |
| $23$ | $0.743$ | $0.881$ | $0.804$ | $0.815$ | $0.830$ | $0.980$ | $0.950$ | $0.858$ |
| $23$ | $0.706$ | $0.806$ | $0.824$ | $0.745$ | $0.843$ | $0.861$ | $0.916$ | $0.814$ |
| $23$ | $0.745$ | $0.879$ | $0.679$ | $0.781$ | $0.852$ | $0.942$ | $0.928$ | $0.829$ |
| $16$ | $0.734$ | $0.847$ | $0.665$ | $0.746$ | $0.872$ | $0.926$ | $0.957$ | $0.821$ |
| $16$ | $0.716$ | $0.759$ | $0.659$ | $0.669$ | $0.878$ | $0.830$ | $0.909$ | $0.774$ |
| $16$ | $0.756$ | $0.879$ | $0.658$ | $0.749$ | $0.881$ | $0.972$ | $0.964$ | $0.837$ |
| $16$ | $0.758$ | $0.889$ | $0.722$ | $0.781$ | $0.899$ | $0.977$ | $0.965$ | $0.856$ |
| $16$ | $0.733$ | $0.869$ | $0.709$ | $0.752$ | $0.891$ | $0.942$ | $0.963$ | $0.837$ |
| $16$ | $0.769$ | $0.867$ | $0.772$ | $0.760$ | $0.899$ | $0.957$ | $0.956$ | $0.854$ |
| $16$ | $0.717$ | $0.834$ | $0.772$ | $0.669$ | $0.853$ | $0.830$ | $0.904$ | $0.797$ |
| $16$ | $0.622$ | $0.556$ | $0.764$ | $0.582$ | $0.762$ | $0.670$ | $0.778$ | $0.676$ |
| $16$ | $0.729$ | $0.875$ | $0.722$ | $0.736$ | $0.888$ | $0.954$ | $0.964$ | $0.838$ |
| $16$ | $0.639$ | $0.594$ | $0.666$ | $0.567$ | $0.776$ | $0.758$ | $0.801$ | $0.686$ |
| $11$ | $0.663$ | $0.681$ | $0.656$ | $0.583$ | $0.808$ | $0.751$ | $0.814$ | $0.708$ |
| $11$ | $0.694$ | $0.851$ | $0.748$ | $0.724$ | $0.763$ | $0.868$ | $0.929$ | $0.797$ |
| $11$ | $0.720$ | $0.862$ | $0.746$ | $0.726$ | $0.850$ | $0.890$ | $0.945$ | $0.820$ |
| $11$ | $0.722$ | $0.767$ | $0.687$ | $0.668$ | $0.869$ | $0.782$ | $0.898$ | $0.770$ |
| $11$ | $0.696$ | $0.777$ | $0.719$ | $0.732$ | $0.760$ | $0.832$ | $0.901$ | $0.774$ |
| $11$ | $0.714$ | $0.806$ | $0.644$ | $0.638$ | $0.822$ | $0.796$ | $0.854$ | $0.754$ |
| $11$ | $0.654$ | $0.767$ | $0.619$ | $0.627$ | $0.818$ | $0.742$ | $0.822$ | $0.721$ |
| $11$ | $0.715$ | $0.823$ | $0.738$ | $0.721$ | $0.813$ | $0.936$ | $0.949$ | $0.814$ |
| $11$ | $0.680$ | $0.712$ | $0.721$ | $0.579$ | $0.852$ | $0.744$ | $0.791$ | $0.726$ |
| $11$ | $0.713$ | $0.756$ | $0.753$ | $0.708$ | $0.756$ | $0.858$ | $0.835$ | $0.768$ |
| $8$ | $0.659$ | $0.623$ | $0.615$ | $0.588$ | $0.783$ | $0.695$ | $0.796$ | $0.680$ |
| $8$ | $0.550$ | $0.721$ | $0.608$ | $0.555$ | $0.599$ | $0.692$ | $0.692$ | $0.631$ |
| $8$ | $0.716$ | $0.745$ | $0.622$ | $0.698$ | $0.848$ | $0.894$ | $0.875$ | $0.771$ |
| $8$ | $0.678$ | $0.794$ | $0.649$ | $0.704$ | $0.664$ | $0.946$ | $0.926$ | $0.766$ |
| $8$ | $0.674$ | $0.607$ | $0.617$ | $0.557$ | $0.890$ | $0.735$ | $0.798$ | $0.697$ |
| $8$ | $0.667$ | $0.689$ | $0.626$ | $0.631$ | $0.747$ | $0.684$ | $0.806$ | $0.693$ |
| $8$ | $0.673$ | $0.730$ | $0.725$ | $0.688$ | $0.781$ | $0.825$ | $0.885$ | $0.758$ |
| $8$ | $0.690$ | $0.785$ | $0.546$ | $0.671$ | $0.747$ | $0.918$ | $0.837$ | $0.742$ |
| $8$ | $0.690$ | $0.795$ | $0.581$ | $0.651$ | $0.877$ | $0.730$ | $0.827$ | $0.736$ |
| $8$ | $0.524$ | $0.480$ | $0.688$ | $0.488$ | $0.595$ | $0.723$ | $0.726$ | $0.603$ |
| $5$ | $0.586$ | $0.441$ | $0.636$ | $0.533$ | $0.758$ | $0.662$ | $0.739$ | $0.622$ |
| $5$ | $0.610$ | $0.518$ | $0.590$ | $0.542$ | $0.788$ | $0.700$ | $0.765$ | $0.645$ |
| $5$ | $0.663$ | $0.686$ | $0.682$ | $0.598$ | $0.854$ | $0.785$ | $0.832$ | $0.728$ |
| $5$ | $0.592$ | $0.666$ | $0.718$ | $0.591$ | $0.643$ | $0.857$ | $0.770$ | $0.691$ |
| $5$ | $0.689$ | $0.771$ | $0.658$ | $0.685$ | $0.673$ | $0.853$ | $0.689$ | $0.717$ |
| $5$ | $0.548$ | $0.475$ | $0.566$ | $0.542$ | $0.744$ | $0.652$ | $0.624$ | $0.593$ |
| $5$ | $0.627$ | $0.561$ | $0.633$ | $0.563$ | $0.750$ | $0.659$ | $0.645$ | $0.634$ |
| $5$ | $0.696$ | $0.811$ | $0.664$ | $0.696$ | $0.802$ | $0.814$ | $0.834$ | $0.760$ |
| $5$ | $0.676$ | $0.686$ | $0.690$ | $0.615$ | $0.651$ | $0.764$ | $0.822$ | $0.701$ |
| $5$ | $0.713$ | $0.758$ | $0.565$ | $0.671$ | $0.862$ | $0.898$ | $0.890$ | $0.765$ |

**Table 13: Full experimental results measured by $\mathrm{AUC}$ for scaling law with varying numbers of deepfake methods. The concluded scaling law is shown in Fig. [3](https://arxiv.org/html/2510.16320v1#S4.F3) (Right).**
| $\mathrm{AUC}$ | DFD | CDF V2 | Wild | Forgery. | DFF | DF40 | ScaleDF | Mean |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| $88$ | $0.793$ | $0.915$ | $0.815$ | $0.824$ | $0.909$ | $0.980$ | $0.968$ | $0.886$ |
| $64$ | $0.774$ | $0.914$ | $0.776$ | $0.815$ | $0.874$ | $0.982$ | $0.965$ | $0.871$ |
| $64$ | $0.756$ | $0.912$ | $0.802$ | $0.820$ | $0.893$ | $0.978$ | $0.955$ | $0.874$ |
| $64$ | $0.790$ | $0.917$ | $0.802$ | $0.836$ | $0.888$ | $0.981$ | $0.963$ | $0.882$ |
| $64$ | $0.782$ | $0.915$ | $0.796$ | $0.823$ | $0.888$ | $0.977$ | $0.963$ | $0.878$ |
| $64$ | $0.766$ | $0.908$ | $0.806$ | $0.802$ | $0.894$ | $0.978$ | $0.965$ | $0.874$ |
| $64$ | $0.755$ | $0.853$ | $0.809$ | $0.803$ | $0.858$ | $0.973$ | $0.967$ | $0.860$ |
| $64$ | $0.756$ | $0.873$ | $0.823$ | $0.795$ | $0.818$ | $0.977$ | $0.959$ | $0.857$ |
| $64$ | $0.779$ | $0.899$ | $0.800$ | $0.804$ | $0.899$ | $0.977$ | $0.960$ | $0.874$ |
| $64$ | $0.766$ | $0.894$ | $0.828$ | $0.808$ | $0.909$ | $0.977$ | $0.960$ | $0.877$ |
| $64$ | $0.788$ | $0.909$ | $0.819$ | $0.819$ | $0.891$ | $0.975$ | $0.964$ | $0.881$ |
| $45$ | $0.777$ | $0.886$ | $0.794$ | $0.798$ | $0.761$ | $0.975$ | $0.954$ | $0.849$ |
| $45$ | $0.766$ | $0.906$ | $0.804$ | $0.792$ | $0.781$ | $0.970$ | $0.954$ | $0.853$ |
| $45$ | $0.746$ | $0.909$ | $0.748$ | $0.802$ | $0.772$ | $0.974$ | $0.966$ | $0.845$ |
| $45$ | $0.760$ | $0.915$ | $0.754$ | $0.791$ | $0.893$ | $0.967$ | $0.946$ | $0.861$ |
| $45$ | $0.763$ | $0.904$ | $0.791$ | $0.810$ | $0.796$ | $0.979$ | $0.953$ | $0.857$ |
| $45$ | $0.684$ | $0.824$ | $0.800$ | $0.774$ | $0.882$ | $0.963$ | $0.959$ | $0.841$ |
| $45$ | $0.673$ | $0.854$ | $0.775$ | $0.792$ | $0.842$ | $0.976$ | $0.958$ | $0.839$ |
| $45$ | $0.701$ | $0.897$ | $0.768$ | $0.778$ | $0.768$ | $0.968$ | $0.937$ | $0.831$ |
| $45$ | $0.764$ | $0.908$ | $0.809$ | $0.811$ | $0.829$ | $0.970$ | $0.956$ | $0.864$ |
| $45$ | $0.796$ | $0.912$ | $0.816$ | $0.817$ | $0.816$ | $0.969$ | $0.955$ | $0.869$ |
| $32$ | $0.720$ | $0.794$ | $0.758$ | $0.790$ | $0.855$ | $0.973$ | $0.949$ | $0.834$ |
| $32$ | $0.767$ | $0.869$ | $0.799$ | $0.751$ | $0.856$ | $0.969$ | $0.942$ | $0.850$ |
| $32$ | $0.679$ | $0.877$ | $0.754$ | $0.765$ | $0.718$ | $0.959$ | $0.947$ | $0.814$ |
| $32$ | $0.669$ | $0.839$ | $0.752$ | $0.743$ | $0.853$ | $0.947$ | $0.950$ | $0.822$ |
| $32$ | $0.685$ | $0.836$ | $0.740$ | $0.762$ | $0.870$ | $0.970$ | $0.949$ | $0.830$ |
| $32$ | $0.740$ | $0.892$ | $0.741$ | $0.764$ | $0.794$ | $0.952$ | $0.931$ | $0.830$ |
| $32$ | $0.734$ | $0.845$ | $0.773$ | $0.765$ | $0.774$ | $0.951$ | $0.915$ | $0.822$ |
| $32$ | $0.713$ | $0.804$ | $0.819$ | $0.768$ | $0.722$ | $0.968$ | $0.934$ | $0.818$ |
| $32$ | $0.742$ | $0.842$ | $0.782$ | $0.772$ | $0.818$ | $0.969$ | $0.952$ | $0.840$ |
| $32$ | $0.748$ | $0.910$ | $0.708$ | $0.767$ | $0.759$ | $0.968$ | $0.944$ | $0.829$ |
| $22$ | $0.667$ | $0.891$ | $0.733$ | $0.791$ | $0.663$ | $0.965$ | $0.935$ | $0.806$ |
| $22$ | $0.662$ | $0.829$ | $0.729$ | $0.732$ | $0.854$ | $0.959$ | $0.924$ | $0.813$ |
| $22$ | $0.736$ | $0.836$ | $0.772$ | $0.736$ | $0.725$ | $0.957$ | $0.909$ | $0.810$ |
| $22$ | $0.625$ | $0.774$ | $0.744$ | $0.726$ | $0.857$ | $0.945$ | $0.923$ | $0.799$ |
| $22$ | $0.736$ | $0.842$ | $0.746$ | $0.803$ | $0.880$ | $0.966$ | $0.909$ | $0.840$ |
| $22$ | $0.648$ | $0.813$ | $0.651$ | $0.758$ | $0.654$ | $0.971$ | $0.932$ | $0.775$ |
| $22$ | $0.669$ | $0.826$ | $0.710$ | $0.744$ | $0.858$ | $0.958$ | $0.926$ | $0.813$ |
| $22$ | $0.583$ | $0.743$ | $0.717$ | $0.727$ | $0.700$ | $0.957$ | $0.929$ | $0.765$ |
| $22$ | $0.744$ | $0.801$ | $0.779$ | $0.766$ | $0.726$ | $0.960$ | $0.933$ | $0.816$ |
| $22$ | $0.708$ | $0.813$ | $0.771$ | $0.763$ | $0.618$ | $0.943$ | $0.899$ | $0.788$ |
| $16$ | $0.626$ | $0.822$ | $0.708$ | $0.749$ | $0.677$ | $0.956$ | $0.923$ | $0.780$ |
| $16$ | $0.609$ | $0.728$ | $0.698$ | $0.693$ | $0.854$ | $0.957$ | $0.942$ | $0.783$ |
| $16$ | $0.779$ | $0.899$ | $0.755$ | $0.804$ | $0.724$ | $0.870$ | $0.874$ | $0.815$ |
| $16$ | $0.566$ | $0.656$ | $0.707$ | $0.697$ | $0.643$ | $0.956$ | $0.893$ | $0.731$ |
| $16$ | $0.590$ | $0.674$ | $0.686$ | $0.716$ | $0.720$ | $0.963$ | $0.937$ | $0.755$ |
| $16$ | $0.701$ | $0.810$ | $0.766$ | $0.778$ | $0.634$ | $0.955$ | $0.916$ | $0.794$ |
| $16$ | $0.660$ | $0.827$ | $0.718$ | $0.739$ | $0.659$ | $0.968$ | $0.912$ | $0.783$ |
| $16$ | $0.556$ | $0.674$ | $0.684$ | $0.660$ | $0.858$ | $0.954$ | $0.916$ | $0.757$ |
| $16$ | $0.617$ | $0.804$ | $0.684$ | $0.699$ | $0.722$ | $0.943$ | $0.903$ | $0.767$ |
| $16$ | $0.677$ | $0.829$ | $0.644$ | $0.703$ | $0.609$ | $0.907$ | $0.893$ | $0.752$ |
| $11$ | $0.555$ | $0.662$ | $0.718$ | $0.678$ | $0.862$ | $0.956$ | $0.906$ | $0.763$ |
| $11$ | $0.604$ | $0.775$ | $0.689$ | $0.720$ | $0.656$ | $0.880$ | $0.890$ | $0.745$ |
| $11$ | $0.710$ | $0.748$ | $0.763$ | $0.754$ | $0.593$ | $0.942$ | $0.880$ | $0.770$ |
| $11$ | $0.706$ | $0.814$ | $0.751$ | $0.774$ | $0.744$ | $0.965$ | $0.887$ | $0.806$ |
| $11$ | $0.740$ | $0.848$ | $0.723$ | $0.756$ | $0.519$ | $0.956$ | $0.901$ | $0.778$ |
| $11$ | $0.687$ | $0.826$ | $0.606$ | $0.752$ | $0.510$ | $0.910$ | $0.865$ | $0.736$ |
| $11$ | $0.686$ | $0.756$ | $0.743$ | $0.768$ | $0.607$ | $0.858$ | $0.861$ | $0.754$ |
| $11$ | $0.662$ | $0.771$ | $0.594$ | $0.714$ | $0.537$ | $0.919$ | $0.862$ | $0.723$ |
| $11$ | $0.592$ | $0.745$ | $0.603$ | $0.708$ | $0.649$ | $0.905$ | $0.898$ | $0.728$ |
| $11$ | $0.742$ | $0.806$ | $0.743$ | $0.746$ | $0.795$ | $0.906$ | $0.856$ | $0.799$ |

**Table 14: Full experimental results measured by $\mathrm{AUC}$ for scaling law with varying numbers of training images. The concluded scaling law is shown in Fig. [4](https://arxiv.org/html/2510.16320v1#S4.F4) (Left).**
| $\mathrm{AUC}$ | DFD | CDF V2 | Wild | Forgery. | DFF | DF40 | ScaleDF | Mean |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| $1.4\times 10^{7}$ | $0.793$ | $0.915$ | $0.815$ | $0.824$ | $0.909$ | $0.980$ | $0.968$ | $0.886$ |
| $3.7\times 10^{6}$ | $0.788$ | $0.904$ | $0.831$ | $0.792$ | $0.842$ | $0.964$ | $0.949$ | $0.867$ |
| $3.7\times 10^{6}$ | $0.778$ | $0.903$ | $0.827$ | $0.790$ | $0.840$ | $0.966$ | $0.948$ | $0.864$ |
| $3.7\times 10^{6}$ | $0.781$ | $0.905$ | $0.820$ | $0.791$ | $0.840$ | $0.962$ | $0.946$ | $0.864$ |
| $3.7\times 10^{6}$ | $0.777$ | $0.899$ | $0.823$ | $0.790$ | $0.843$ | $0.967$ | $0.950$ | $0.864$ |
| $3.7\times 10^{6}$ | $0.785$ | $0.904$ | $0.820$ | $0.791$ | $0.838$ | $0.963$ | $0.946$ | $0.864$ |
| $1.4\times 10^{6}$ | $0.754$ | $0.873$ | $0.828$ | $0.756$ | $0.804$ | $0.952$ | $0.943$ | $0.844$ |
| $1.4\times 10^{6}$ | $0.763$ | $0.876$ | $0.828$ | $0.758$ | $0.801$ | $0.949$ | $0.941$ | $0.845$ |
| $1.4\times 10^{6}$ | $0.761$ | $0.880$ | $0.824$ | $0.757$ | $0.805$ | $0.948$ | $0.942$ | $0.845$ |
| $1.4\times 10^{6}$ | $0.760$ | $0.878$ | $0.823$ | $0.756$ | $0.812$ | $0.948$ | $0.940$ | $0.845$ |
| $1.4\times 10^{6}$ | $0.760$ | $0.867$ | $0.823$ | $0.756$ | $0.811$ | $0.950$ | $0.939$ | $0.844$ |
| $9.3\times 10^{5}$ | $0.725$ | $0.850$ | $0.805$ | $0.724$ | $0.776$ | $0.941$ | $0.933$ | $0.822$ |
| $9.3\times 10^{5}$ | $0.734$ | $0.845$ | $0.803$ | $0.727$ | $0.780$ | $0.943$ | $0.933$ | $0.824$ |
| $9.3\times 10^{5}$ | $0.735$ | $0.850$ | $0.804$ | $0.723$ | $0.781$ | $0.939$ | $0.932$ | $0.824$ |
| $9.3\times 10^{5}$ | $0.726$ | $0.849$ | $0.801$ | $0.722$ | $0.776$ | $0.936$ | $0.928$ | $0.820$ |
| $9.3\times 10^{5}$ | $0.737$ | $0.854$ | $0.803$ | $0.726$ | $0.778$ | $0.941$ | $0.932$ | $0.824$ |
| $2.3\times 10^{5}$ | $0.624$ | $0.668$ | $0.718$ | $0.614$ | $0.708$ | $0.830$ | $0.848$ | $0.716$ |
| $2.3\times 10^{5}$ | $0.628$ | $0.676$ | $0.716$ | $0.616$ | $0.714$ | $0.843$ | $0.855$ | $0.721$ |
| $2.3\times 10^{5}$ | $0.630$ | $0.673$ | $0.724$ | $0.617$ | $0.713$ | $0.826$ | $0.850$ | $0.719$ |
| $2.3\times 10^{5}$ | $0.628$ | $0.679$ | $0.721$ | $0.616$ | $0.710$ | $0.831$ | $0.851$ | $0.719$ |
| $2.3\times 10^{5}$ | $0.614$ | $0.669$ | $0.727$ | $0.615$ | $0.713$ | $0.830$ | $0.853$ | $0.717$ |
| $1.4\times 10^{5}$ | $0.608$ | $0.654$ | $0.699$ | $0.596$ | $0.706$ | $0.744$ | $0.786$ | $0.685$ |
| $1.4\times 10^{5}$ | $0.602$ | $0.648$ | $0.680$ | $0.597$ | $0.696$ | $0.735$ | $0.782$ | $0.677$ |
| $1.4\times 10^{5}$ | $0.605$ | $0.644$ | $0.698$ | $0.596$ | $0.699$ | $0.732$ | $0.784$ | $0.680$ |
| $1.4\times 10^{5}$ | $0.604$ | $0.655$ | $0.696$ | $0.599$ | $0.706$ | $0.731$ | $0.782$ | $0.682$ |
| $1.4\times 10^{5}$ | $0.604$ | $0.645$ | $0.679$ | $0.598$ | $0.697$ | $0.734$ | $0.787$ | $0.678$ |
| $5.8\times 10^{4}$ | $0.551$ | $0.536$ | $0.570$ | $0.554$ | $0.652$ | $0.464$ | $0.656$ | $0.569$ |
| $5.8\times 10^{4}$ | $0.550$ | $0.545$ | $0.573$ | $0.555$ | $0.657$ | $0.470$ | $0.661$ | $0.573$ |
| $5.8\times 10^{4}$ | $0.558$ | $0.542$ | $0.565$ | $0.556$ | $0.656$ | $0.474$ | $0.659$ | $0.573$ |
| $5.8\times 10^{4}$ | $0.553$ | $0.548$ | $0.579$ | $0.555$ | $0.651$ | $0.464$ | $0.658$ | $0.573$ |
| $5.8\times 10^{4}$ | $0.552$ | $0.542$ | $0.561$ | $0.550$ | $0.644$ | $0.450$ | $0.654$ | $0.565$ |
| $1.4\times 10^{4}$ | $0.509$ | $0.509$ | $0.530$ | $0.511$ | $0.580$ | $0.344$ | $0.548$ | $0.504$ |
| $1.4\times 10^{4}$ | $0.507$ | $0.491$ | $0.539$ | $0.511$ | $0.581$ | $0.335$ | $0.550$ | $0.502$ |
| $1.4\times 10^{4}$ | $0.506$ | $0.492$ | $0.536$ | $0.513$ | $0.581$ | $0.353$ | $0.555$ | $0.505$ |
| $1.4\times 10^{4}$ | $0.506$ | $0.501$ | $0.527$ | $0.511$ | $0.583$ | $0.350$ | $0.555$ | $0.505$ |
| $1.4\times 10^{4}$ | $0.508$ | $0.489$ | $0.535$ | $0.511$ | $0.586$ | $0.339$ | $0.551$ | $0.503$ |

**Table 16: Full experimental results measured by $\mathrm{EER}$ for scaling law with varying numbers of real domains. The concluded scaling law is shown in Fig. [5](https://arxiv.org/html/2510.16320v1#A3.F5) (Left Top).**
| $\mathrm{EER}$ | DFD | CDF V2 | Wild | Forgery. | DFF | DF40 | ScaleDF | Mean |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| $46$ | $0.281$ | $0.162$ | $0.268$ | $0.260$ | $0.161$ | $0.063$ | $0.093$ | $0.184$ |
| $32$ | $0.317$ | $0.224$ | $0.337$ | $0.310$ | $0.189$ | $0.149$ | $0.092$ | $0.231$ |
| $32$ | $0.308$ | $0.162$ | $0.313$ | $0.264$ | $0.162$ | $0.055$ | $0.092$ | $0.194$ |
| $32$ | $0.293$ | $0.173$ | $0.326$ | $0.265$ | $0.148$ | $0.063$ | $0.093$ | $0.194$ |
| $32$ | $0.307$ | $0.179$ | $0.257$ | $0.269$ | $0.209$ | $0.070$ | $0.105$ | $0.199$ |
| $32$ | $0.308$ | $0.173$ | $0.334$ | $0.270$ | $0.210$ | $0.064$ | $0.104$ | $0.209$ |
| $32$ | $0.291$ | $0.165$ | $0.336$ | $0.275$ | $0.158$ | $0.069$ | $0.098$ | $0.199$ |
| $32$ | $0.344$ | $0.221$ | $0.262$ | $0.336$ | $0.229$ | $0.250$ | $0.152$ | $0.256$ |
| $32$ | $0.295$ | $0.170$ | $0.246$ | $0.287$ | $0.166$ | $0.078$ | $0.107$ | $0.193$ |
| $32$ | $0.330$ | $0.240$ | $0.268$ | $0.297$ | $0.197$ | $0.158$ | $0.102$ | $0.228$ |
| $32$ | $0.309$ | $0.183$ | $0.261$ | $0.287$ | $0.168$ | $0.134$ | $0.102$ | $0.206$ |
| $23$ | $0.316$ | $0.192$ | $0.285$ | $0.276$ | $0.227$ | $0.127$ | $0.135$ | $0.223$ |
| $23$ | $0.331$ | $0.250$ | $0.288$ | $0.334$ | $0.197$ | $0.194$ | $0.146$ | $0.249$ |
| $23$ | $0.312$ | $0.196$ | $0.314$ | $0.306$ | $0.214$ | $0.125$ | $0.105$ | $0.225$ |
| $23$ | $0.320$ | $0.174$ | $0.335$ | $0.302$ | $0.209$ | $0.069$ | $0.127$ | $0.220$ |
| $23$ | $0.351$ | $0.225$ | $0.310$ | $0.316$ | $0.267$ | $0.136$ | $0.130$ | $0.248$ |
| $23$ | $0.304$ | $0.190$ | $0.281$ | $0.294$ | $0.246$ | $0.075$ | $0.123$ | $0.216$ |
| $23$ | $0.285$ | $0.189$ | $0.268$ | $0.312$ | $0.176$ | $0.081$ | $0.096$ | $0.201$ |
| $23$ | $0.327$ | $0.196$ | $0.272$ | $0.271$ | $0.243$ | $0.065$ | $0.127$ | $0.215$ |
| $23$ | $0.343$ | $0.273$ | $0.256$ | $0.327$ | $0.227$ | $0.215$ | $0.170$ | $0.259$ |
| $23$ | $0.319$ | $0.199$ | $0.374$ | $0.295$ | $0.228$ | $0.131$ | $0.153$ | $0.243$ |
| $16$ | $0.335$ | $0.226$ | $0.355$ | $0.319$ | $0.202$ | $0.156$ | $0.109$ | $0.243$ |
| $16$ | $0.342$ | $0.308$ | $0.373$ | $0.378$ | $0.189$ | $0.235$ | $0.165$ | $0.284$ |
| $16$ | $0.320$ | $0.200$ | $0.393$ | $0.311$ | $0.190$ | $0.083$ | $0.091$ | $0.227$ |
| $16$ | $0.311$ | $0.188$ | $0.335$ | $0.292$ | $0.172$ | $0.064$ | $0.089$ | $0.208$ |
| $16$ | $0.333$ | $0.205$ | $0.334$ | $0.314$ | $0.179$ | $0.129$ | $0.093$ | $0.227$ |
| $16$ | $0.299$ | $0.198$ | $0.293$ | $0.309$ | $0.172$ | $0.097$ | $0.093$ | $0.209$ |
| $16$ | $0.342$ | $0.238$ | $0.295$ | $0.376$ | $0.223$ | $0.250$ | $0.169$ | $0.270$ |
| $16$ | $0.415$ | $0.457$ | $0.287$ | $0.444$ | $0.295$ | $0.358$ | $0.312$ | $0.367$ |
| $16$ | $0.337$ | $0.201$ | $0.335$ | $0.326$ | $0.182$ | $0.110$ | $0.092$ | $0.226$ |
| $16$ | $0.404$ | $0.436$ | $0.388$ | $0.460$ | $0.286$ | $0.293$ | $0.302$ | $0.367$ |
| $11$ | $0.387$ | $0.374$ | $0.387$ | $0.447$ | $0.256$ | $0.297$ | $0.282$ | $0.347$ |
| $11$ | $0.358$ | $0.229$ | $0.310$ | $0.335$ | $0.300$ | $0.198$ | $0.140$ | $0.267$ |
| $11$ | $0.331$ | $0.218$ | $0.308$ | $0.333$ | $0.222$ | $0.191$ | $0.119$ | $0.246$ |
| $11$ | $0.337$ | $0.293$ | $0.369$ | $0.377$ | $0.201$ | $0.284$ | $0.158$ | $0.288$ |
| $11$ | $0.347$ | $0.294$ | $0.318$ | $0.335$ | $0.299$ | $0.241$ | $0.170$ | $0.286$ |
| $11$ | $0.337$ | $0.257$ | $0.402$ | $0.397$ | $0.252$ | $0.280$ | $0.208$ | $0.305$ |
| $11$ | $0.396$ | $0.295$ | $0.421$ | $0.412$ | $0.255$ | $0.324$ | $0.254$ | $0.337$ |
| $11$ | $0.351$ | $0.250$ | $0.316$ | $0.337$ | $0.255$ | $0.143$ | $0.118$ | $0.253$ |
| $11$ | $0.374$ | $0.344$ | $0.323$ | $0.445$ | $0.218$ | $0.313$ | $0.286$ | $0.329$ |
| $11$ | $0.339$ | $0.312$ | $0.315$ | $0.354$ | $0.311$ | $0.224$ | $0.237$ | $0.299$ |
| $8$ | $0.383$ | $0.421$ | $0.421$ | $0.442$ | $0.292$ | $0.364$ | $0.251$ | $0.367$ |
| $8$ | $0.469$ | $0.337$ | $0.410$ | $0.464$ | $0.422$ | $0.359$ | $0.360$ | $0.403$ |
| $8$ | $0.348$ | $0.314$ | $0.402$ | $0.353$ | $0.222$ | $0.185$ | $0.180$ | $0.286$ |
| $8$ | $0.378$ | $0.278$ | $0.402$ | $0.351$ | $0.384$ | $0.122$ | $0.150$ | $0.295$ |
| $8$ | $0.376$ | $0.428$ | $0.406$ | $0.465$ | $0.177$ | $0.317$ | $0.294$ | $0.352$ |
| $8$ | $0.379$ | $0.369$ | $0.385$ | $0.406$ | $0.308$ | $0.354$ | $0.268$ | $0.353$ |
| $8$ | $0.372$ | $0.336$ | $0.324$ | $0.367$ | $0.282$ | $0.247$ | $0.194$ | $0.303$ |
| $8$ | $0.370$ | $0.276$ | $0.464$ | $0.376$ | $0.312$ | $0.155$ | $0.214$ | $0.310$ |
| $8$ | $0.358$ | $0.270$ | $0.422$ | $0.393$ | $0.195$ | $0.326$ | $0.222$ | $0.312$ |
| $8$ | $0.484$ | $0.514$ | $0.354$ | $0.512$ | $0.427$ | $0.333$ | $0.342$ | $0.424$ |
| $5$ | $0.438$ | $0.542$ | $0.397$ | $0.481$ | $0.294$ | $0.373$ | $0.324$ | $0.407$ |
| $5$ | $0.426$ | $0.486$ | $0.431$ | $0.476$ | $0.267$ | $0.344$ | $0.311$ | $0.392$ |
| $5$ | $0.383$ | $0.374$ | $0.363$ | $0.435$ | $0.220$ | $0.275$ | $0.260$ | $0.330$ |
| $5$ | $0.435$ | $0.384$ | $0.337$ | $0.439$ | $0.390$ | $0.217$ | $0.302$ | $0.358$ |
| $5$ | $0.359$ | $0.300$ | $0.390$ | $0.371$ | $0.366$ | $0.227$ | $0.369$ | $0.340$ |
| $5$ | $0.471$ | $0.516$ | $0.448$ | $0.481$ | $0.326$ | $0.382$ | $0.413$ | $0.434$ |
| $5$ | $0.408$ | $0.466$ | $0.389$ | $0.462$ | $0.310$ | $0.385$ | $0.388$ | $0.401$ |
| $5$ | $0.356$ | $0.260$ | $0.380$ | $0.356$ | $0.265$ | $0.253$ | $0.242$ | $0.302$ |
| $5$ | $0.368$ | $0.369$ | $0.361$ | $0.422$ | $0.396$ | $0.299$ | $0.264$ | $0.354$ |
| $5$ | $0.339$ | $0.309$ | $0.457$ | $0.371$ | $0.206$ | $0.176$ | $0.159$ | $0.288$ |

**Table 17: Full experimental results measured by $\mathrm{EER}$ for scaling law with varying numbers of deepfake methods. The concluded scaling law is shown in Fig. [5](https://arxiv.org/html/2510.16320v1#A3.F5) (Right Top).**
| $\mathrm{EER}$ | DFD | CDF V2 | Wild | Forgery. | DFF | DF40 | ScaleDF | Mean |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| $88$ | $0.281$ | $0.162$ | $0.268$ | $0.260$ | $0.161$ | $0.063$ | $0.093$ | $0.184$ |
| $64$ | $0.299$ | $0.165$ | $0.301$ | $0.268$ | $0.200$ | $0.061$ | $0.096$ | $0.199$ |
| $64$ | $0.312$ | $0.167$ | $0.279$ | $0.264$ | $0.175$ | $0.066$ | $0.112$ | $0.196$ |
| $64$ | $0.283$ | $0.162$ | $0.277$ | $0.249$ | $0.186$ | $0.064$ | $0.099$ | $0.189$ |
| $64$ | $0.293$ | $0.162$ | $0.272$ | $0.261$ | $0.183$ | $0.067$ | $0.100$ | $0.191$ |
| $64$ | $0.304$ | $0.172$ | $0.278$ | $0.278$ | $0.178$ | $0.068$ | $0.096$ | $0.196$ |
| $64$ | $0.310$ | $0.222$ | $0.266$ | $0.278$ | $0.216$ | $0.077$ | $0.094$ | $0.209$ |
| $64$ | $0.314$ | $0.208$ | $0.255$ | $0.286$ | $0.253$ | $0.071$ | $0.106$ | $0.213$ |
| $64$ | $0.294$ | $0.180$ | $0.274$ | $0.278$ | $0.173$ | $0.070$ | $0.107$ | $0.197$ |
| $64$ | $0.303$ | $0.183$ | $0.251$ | $0.273$ | $0.162$ | $0.070$ | $0.106$ | $0.193$ |
| $64$ | $0.287$ | $0.169$ | $0.257$ | $0.264$ | $0.182$ | $0.073$ | $0.097$ | $0.190$ |
| $45$ | $0.298$ | $0.197$ | $0.276$ | $0.282$ | $0.304$ | $0.073$ | $0.113$ | $0.221$ |
| $45$ | $0.306$ | $0.171$ | $0.270$ | $0.286$ | $0.291$ | $0.080$ | $0.118$ | $0.217$ |
| $45$ | $0.318$ | $0.171$ | $0.309$ | $0.277$ | $0.289$ | $0.077$ | $0.092$ | $0.219$ |
| $45$ | $0.307$ | $0.159$ | $0.309$ | $0.287$ | $0.180$ | $0.088$ | $0.121$ | $0.207$ |
| $45$ | $0.307$ | $0.177$ | $0.285$ | $0.270$ | $0.269$ | $0.067$ | $0.114$ | $0.213$ |
| $45$ | $0.372$ | $0.251$ | $0.275$ | $0.301$ | $0.194$ | $0.095$ | $0.107$ | $0.228$ |
| $45$ | $0.376$ | $0.228$ | $0.292$ | $0.286$ | $0.230$ | $0.075$ | $0.109$ | $0.228$ |
| $45$ | $0.363$ | $0.185$ | $0.301$ | $0.298$ | $0.293$ | $0.086$ | $0.139$ | $0.238$ |
| $45$ | $0.307$ | $0.172$ | $0.261$ | $0.272$ | $0.243$ | $0.085$ | $0.110$ | $0.207$ |
| $45$ | $0.280$ | $0.164$ | $0.265$ | $0.268$ | $0.262$ | $0.082$ | $0.114$ | $0.205$ |
| $32$ | $0.340$ | $0.279$ | $0.311$ | $0.289$ | $0.220$ | $0.081$ | $0.122$ | $0.235$ |
| $32$ | $0.312$ | $0.216$ | $0.274$ | $0.323$ | $0.221$ | $0.085$ | $0.129$ | $0.223$ |
| $32$ | $0.372$ | $0.205$ | $0.309$ | $0.309$ | $0.340$ | $0.096$ | $0.122$ | $0.250$ |
| $32$ | $0.379$ | $0.240$ | $0.307$ | $0.326$ | $0.222$ | $0.117$ | $0.123$ | $0.245$ |
| $32$ | $0.370$ | $0.241$ | $0.315$ | $0.313$ | $0.210$ | $0.085$ | $0.122$ | $0.237$ |
| $32$ | $0.326$ | $0.188$ | $0.317$ | $0.310$ | $0.282$ | $0.114$ | $0.145$ | $0.240$ |
| $32$ | $0.329$ | $0.233$ | $0.294$ | $0.309$ | $0.297$ | $0.112$ | $0.164$ | $0.248$ |
| $32$ | $0.345$ | $0.272$ | $0.256$ | $0.306$ | $0.337$ | $0.092$ | $0.146$ | $0.251$ |
| $32$ | $0.321$ | $0.241$ | $0.293$ | $0.303$ | $0.260$ | $0.085$ | $0.123$ | $0.232$ |
| $32$ | $0.317$ | $0.168$ | $0.344$ | $0.308$ | $0.308$ | $0.089$ | $0.130$ | $0.238$ |
| $22$ | $0.390$ | $0.194$ | $0.330$ | $0.282$ | $0.382$ | $0.090$ | $0.130$ | $0.257$ |
| $22$ | $0.388$ | $0.251$ | $0.325$ | $0.335$ | $0.222$ | $0.101$ | $0.148$ | $0.253$ |
| $22$ | $0.326$ | $0.244$ | $0.298$ | $0.332$ | $0.332$ | $0.100$ | $0.168$ | $0.257$ |
| $22$ | $0.413$ | $0.300$ | $0.311$ | $0.338$ | $0.218$ | $0.125$ | $0.148$ | $0.265$ |
| $22$ | $0.329$ | $0.237$ | $0.317$ | $0.278$ | $0.192$ | $0.088$ | $0.167$ | $0.230$ |
| $22$ | $0.397$ | $0.265$ | $0.375$ | $0.313$ | $0.395$ | $0.081$ | $0.135$ | $0.280$ |
| $22$ | $0.386$ | $0.252$ | $0.344$ | $0.328$ | $0.216$ | $0.102$ | $0.155$ | $0.255$ |
| $22$ | $0.443$ | $0.323$ | $0.333$ | $0.336$ | $0.354$ | $0.104$ | $0.141$ | $0.291$ |
| $22$ | $0.325$ | $0.276$ | $0.288$ | $0.310$ | $0.334$ | $0.105$ | $0.147$ | $0.255$ |
| $22$ | $0.344$ | $0.262$ | $0.299$ | $0.310$ | $0.420$ | $0.120$ | $0.174$ | $0.275$ |
| $16$ | $0.407$ | $0.254$ | $0.345$ | $0.315$ | $0.372$ | $0.106$ | $0.149$ | $0.278$ |
| $16$ | $0.428$ | $0.331$ | $0.347$ | $0.364$ | $0.226$ | $0.109$ | $0.132$ | $0.277$ |
| $16$ | $0.294$ | $0.181$ | $0.309$ | $0.277$ | $0.331$ | $0.190$ | $0.202$ | $0.255$ |
| $16$ | $0.454$ | $0.385$ | $0.352$ | $0.355$ | $0.405$ | $0.101$ | $0.172$ | $0.318$ |
| $16$ | $0.437$ | $0.373$ | $0.357$ | $0.345$ | $0.338$ | $0.092$ | $0.126$ | $0.296$ |
| $16$ | $0.355$ | $0.268$ | $0.294$ | $0.298$ | $0.420$ | $0.109$ | $0.158$ | $0.272$ |
| $16$ | $0.396$ | $0.250$ | $0.331$ | $0.326$ | $0.389$ | $0.080$ | $0.166$ | $0.277$ |
| $16$ | $0.466$ | $0.374$ | $0.354$ | $0.388$ | $0.218$ | $0.115$ | $0.163$ | $0.297$ |
| $16$ | $0.416$ | $0.273$ | $0.366$ | $0.354$ | $0.341$ | $0.122$ | $0.168$ | $0.291$ |
| $16$ | $0.374$ | $0.246$ | $0.390$ | $0.358$ | $0.421$ | $0.157$ | $0.199$ | $0.307$ |
| $11$ | $0.465$ | $0.386$ | $0.334$ | $0.374$ | $0.216$ | $0.111$ | $0.177$ | $0.295$ |
| $11$ | $0.426$ | $0.295$ | $0.366$ | $0.346$ | $0.385$ | $0.210$ | $0.198$ | $0.318$ |
| $11$ | $0.352$ | $0.319$ | $0.302$ | $0.315$ | $0.440$ | $0.124$ | $0.196$ | $0.293$ |
| $11$ | $0.353$ | $0.266$ | $0.310$ | $0.301$ | $0.325$ | $0.091$ | $0.182$ | $0.261$ |
| $11$ | $0.328$ | $0.234$ | $0.335$ | $0.313$ | $0.493$ | $0.106$ | $0.172$ | $0.283$ |
| $11$ | $0.367$ | $0.252$ | $0.420$ | $0.317$ | $0.501$ | $0.158$ | $0.215$ | $0.318$ |
| $11$ | $0.363$ | $0.310$ | $0.323$ | $0.304$ | $0.439$ | $0.191$ | $0.210$ | $0.305$ |
| $11$ | $0.382$ | $0.297$ | $0.435$ | $0.342$ | $0.481$ | $0.151$ | $0.210$ | $0.328$ |
| $11$ | $0.436$ | $0.323$ | $0.434$ | $0.346$ | $0.402$ | $0.165$ | $0.169$ | $0.325$ |
| $11$ | $0.321$ | $0.271$ | $0.332$ | $0.323$ | $0.287$ | $0.160$ | $0.217$ | $0.273$ |

**Table 18: Full experimental results measured by $\mathrm{EER}$ for scaling law with varying numbers of training images. The concluded scaling law is shown in Fig. [5](https://arxiv.org/html/2510.16320v1#A3.F5) (Left Bottom).**
| $\mathrm{EER}$ | DFD | CDF V2 | Wild | Forgery. | DFF | DF40 | ScaleDF | Mean |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| $1.4\times 10^{7}$ | $0.281$ | $0.162$ | $0.268$ | $0.260$ | $0.161$ | $0.063$ | $0.093$ | $0.184$ |
| $3.7\times 10^{6}$ | $0.287$ | $0.176$ | $0.249$ | $0.289$ | $0.234$ | $0.097$ | $0.126$ | $0.208$ |
| $3.7\times 10^{6}$ | $0.294$ | $0.179$ | $0.253$ | $0.292$ | $0.236$ | $0.096$ | $0.128$ | $0.211$ |
| $3.7\times 10^{6}$ | $0.291$ | $0.176$ | $0.259$ | $0.289$ | $0.236$ | $0.099$ | $0.129$ | $0.211$ |
| $3.7\times 10^{6}$ | $0.297$ | $0.183$ | $0.256$ | $0.291$ | $0.232$ | $0.094$ | $0.125$ | $0.211$ |
| $3.7\times 10^{6}$ | $0.289$ | $0.177$ | $0.259$ | $0.289$ | $0.236$ | $0.099$ | $0.130$ | $0.211$ |
| $1.4\times 10^{6}$ | $0.317$ | $0.211$ | $0.257$ | $0.318$ | $0.274$ | $0.114$ | $0.129$ | $0.231$ |
| $1.4\times 10^{6}$ | $0.308$ | $0.208$ | $0.258$ | $0.316$ | $0.277$ | $0.118$ | $0.132$ | $0.231$ |
| $1.4\times 10^{6}$ | $0.310$ | $0.205$ | $0.259$ | $0.318$ | $0.273$ | $0.118$ | $0.132$ | $0.231$ |
| $1.4\times 10^{6}$ | $0.311$ | $0.205$ | $0.263$ | $0.318$ | $0.266$ | $0.120$ | $0.135$ | $0.231$ |
| $1.4\times 10^{6}$ | $0.313$ | $0.216$ | $0.262$ | $0.318$ | $0.269$ | $0.117$ | $0.136$ | $0.233$ |
| $9.3\times 10^{5}$ | $0.340$ | $0.232$ | $0.278$ | $0.341$ | $0.300$ | $0.128$ | $0.140$ | $0.251$ |
| $9.3\times 10^{5}$ | $0.332$ | $0.238$ | $0.281$ | $0.339$ | $0.298$ | $0.128$ | $0.140$ | $0.251$ |
| $9.3\times 10^{5}$ | $0.331$ | $0.233$ | $0.279$ | $0.342$ | $0.296$ | $0.132$ | $0.141$ | $0.250$ |
| $9.3\times 10^{5}$ | $0.339$ | $0.234$ | $0.282$ | $0.342$ | $0.300$ | $0.135$ | $0.146$ | $0.254$ |
| $9.3\times 10^{5}$ | $0.331$ | $0.228$ | $0.280$ | $0.340$ | $0.298$ | $0.129$ | $0.142$ | $0.250$ |
| $2.3\times 10^{5}$ | $0.410$ | $0.383$ | $0.347$ | $0.420$ | $0.353$ | $0.247$ | $0.240$ | $0.343$ |
| $2.3\times 10^{5}$ | $0.408$ | $0.377$ | $0.347$ | $0.418$ | $0.349$ | $0.234$ | $0.234$ | $0.338$ |
| $2.3\times 10^{5}$ | $0.407$ | $0.378$ | $0.338$ | $0.417$ | $0.348$ | $0.249$ | $0.238$ | $0.339$ |
| $2.3\times 10^{5}$ | $0.409$ | $0.374$ | $0.342$ | $0.417$ | $0.350$ | $0.246$ | $0.236$ | $0.339$ |
| $2.3\times 10^{5}$ | $0.419$ | $0.379$ | $0.338$ | $0.418$ | $0.349$ | $0.246$ | $0.234$ | $0.340$ |
| $1.4\times 10^{5}$ | $0.427$ | $0.390$ | $0.360$ | $0.431$ | $0.353$ | $0.319$ | $0.300$ | $0.369$ |
| $1.4\times 10^{5}$ | $0.430$ | $0.393$ | $0.373$ | $0.431$ | $0.359$ | $0.326$ | $0.302$ | $0.373$ |
| $1.4\times 10^{5}$ | $0.429$ | $0.398$ | $0.362$ | $0.432$ | $0.358$ | $0.329$ | $0.301$ | $0.373$ |
| $1.4\times 10^{5}$ | $0.431$ | $0.390$ | $0.364$ | $0.429$ | $0.353$ | $0.329$ | $0.301$ | $0.371$ |
| $1.4\times 10^{5}$ | $0.429$ | $0.397$ | $0.374$ | $0.431$ | $0.358$ | $0.327$ | $0.299$ | $0.374$ |
| $5.8\times 10^{4}$ | $0.470$ | $0.478$ | $0.446$ | $0.459$ | $0.390$ | $0.525$ | $0.405$ | $0.453$ |
| $5.8\times 10^{4}$ | $0.469$ | $0.470$ | $0.447$ | $0.458$ | $0.386$ | $0.520$ | $0.400$ | $0.450$ |
| $5.8\times 10^{4}$ | $0.467$ | $0.475$ | $0.450$ | $0.459$ | $0.387$ | $0.518$ | $0.400$ | $0.451$ |
| $5.8\times 10^{4}$ | $0.468$ | $0.469$ | $0.442$ | $0.458$ | $0.390$ | $0.525$ | $0.401$ | $0.450$ |
| $5.8\times 10^{4}$ | $0.468$ | $0.473$ | $0.453$ | $0.462$ | $0.396$ | $0.533$ | $0.406$ | $0.456$ |
| $1.4\times 10^{4}$ | $0.497$ | $0.494$ | $0.486$ | $0.494$ | $0.443$ | $0.620$ | $0.473$ | $0.501$ |
| $1.4\times 10^{4}$ | $0.498$ | $0.507$ | $0.479$ | $0.492$ | $0.441$ | $0.624$ | $0.473$ | $0.502$ |
| $1.4\times 10^{4}$ | $0.497$ | $0.506$ | $0.481$ | $0.491$ | $0.442$ | $0.611$ | $0.470$ | $0.500$ |
| $1.4\times 10^{4}$ | $0.498$ | $0.500$ | $0.488$ | $0.492$ | $0.439$ | $0.615$ | $0.469$ | $0.500$ |
| $1.4\times 10^{4}$ | $0.497$ | $0.508$ | $0.482$ | $0.493$ | $0.437$ | $0.624$ | $0.473$ | $0.502$ |

**Table 20: Visualization of processed faces in ScaleDF.**
| Name | Demo 0 | Demo 1 | Demo 2 | Demo 3 | Demo 4 |
| --- | --- | --- | --- | --- | --- |
| GRID |  |  |  |  |  |
| MORPH-2 |  |  |  |  |  |
| LFW |  |  |  |  |  |
| Multi-PIE |  |  |  |  |  |
| GENKI-4K |  |  |  |  |  |
| YouTubeFaces |  |  |  |  |  |
| IMFDB |  |  |  |  |  |
| Adience |  |  |  |  |  |
| CACD |  |  |  |  |  |
| CASIA-WebFace |  |  |  |  |  |

**Table 21: Visualization of processed faces in ScaleDF.**
| Name | Demo 0 | Demo 1 | Demo 2 | Demo 3 | Demo 4 |
| --- | --- | --- | --- | --- | --- |
| CREMA-D |  |  |  |  |  |
| FaceScrub |  |  |  |  |  |
| 300VW |  |  |  |  |  |
| CelebA |  |  |  |  |  |
| AFAD |  |  |  |  |  |
| CFPW |  |  |  |  |  |
| WIDER FACE |  |  |  |  |  |
| AffectNet |  |  |  |  |  |
| AgeDB |  |  |  |  |  |
| MAFA |  |  |  |  |  |

**Table 22: Visualization of processed faces in ScaleDF.**
| Name | Demo 0 | Demo 1 | Demo 2 | Demo 3 | Demo 4 |
| --- | --- | --- | --- | --- | --- |
| RAF-DB |  |  |  |  |  |
| UMDFaces |  |  |  |  |  |
| UTKFace |  |  |  |  |  |
| AFEW-VA |  |  |  |  |  |
| MegaAge |  |  |  |  |  |
| AVA |  |  |  |  |  |
| AVSpeech |  |  |  |  |  |
| ExpW |  |  |  |  |  |
| IMDb-Face |  |  |  |  |  |
| RAVDESS |  |  |  |  |  |

**Table 23: Visualization of processed faces in ScaleDF.**
| Name | Demo 0 | Demo 1 | Demo 2 | Demo 3 | Demo 4 |
| --- | --- | --- | --- | --- | --- |
| Tufts Face |  |  |  |  |  |
| VGGFace2 |  |  |  |  |  |
| Celeb-500K |  |  |  |  |  |
| IJB-C |  |  |  |  |  |
| VoxCeleb2 |  |  |  |  |  |
| Aff-Wild2 |  |  |  |  |  |
| FFHQ |  |  |  |  |  |
| FaceForensics++ |  |  |  |  |  |
| BUPT-CBFace |  |  |  |  |  |
| DFEW |  |  |  |  |  |

**Table 24: Visualization of processed faces in ScaleDF.**
| Name | Demo 0 | Demo 1 | Demo 2 | Demo 3 | Demo 4 |
| --- | --- | --- | --- | --- | --- |
| MEAD |  |  |  |  |  |
| MMA |  |  |  |  |  |
| SAMM V3 |  |  |  |  |  |
| FairFace |  |  |  |  |  |
| Glint360K |  |  |  |  |  |
| SpeakingFaces |  |  |  |  |  |
| Wiki-Faces |  |  |  |  |  |
| Asian-Celeb |  |  |  |  |  |
| CelebV-HQ |  |  |  |  |  |
| RMFD |  |  |  |  |  |

**Table 25: Visualization of processed faces in ScaleDF.**
| Name | Demo 0 | Demo 1 | Demo 2 | Demo 3 | Demo 4 |
| --- | --- | --- | --- | --- | --- |
| FaceVid-1K |  |  |  |  |  |
| Faceswap |  |  |  |  |  |
| FaceSwap |  |  |  |  |  |
| DeepFakes |  |  |  |  |  |
| FSGAN |  |  |  |  |  |
| SimSwap |  |  |  |  |  |
| HifiFace |  |  |  |  |  |
| InfoSwap |  |  |  |  |  |
| UniFace |  |  |  |  |  |
| MobileFaceSwap |  |  |  |  |  |

**Table 26: Visualization of processed faces in ScaleDF.**
| Name | Demo 0 | Demo 1 | Demo 2 | Demo 3 | Demo 4 |
| --- | --- | --- | --- | --- | --- |
| E4S |  |  |  |  |  |
| GHOST |  |  |  |  |  |
| BlendFace |  |  |  |  |  |
| FaceDancer |  |  |  |  |  |
| 3DSwap |  |  |  |  |  |
| Inswapper |  |  |  |  |  |
| FaceAdapter |  |  |  |  |  |
| CSCS |  |  |  |  |  |
| REFace |  |  |  |  |  |
| FaceFusion |  |  |  |  |  |

**Table 27: Visualization of processed faces in ScaleDF.**
| Name | Demo 0 | Demo 1 | Demo 2 | Demo 3 | Demo 4 |
| --- | --- | --- | --- | --- | --- |
| InstantID |  |  |  |  |  |
| DiffFace |  |  |  |  |  |
| Face2Face |  |  |  |  |  |
| FOMM |  |  |  |  |  |
| NeuralTextures |  |  |  |  |  |
| OneShot |  |  |  |  |  |
| Face-Vid2Vid |  |  |  |  |  |
| TPSMM |  |  |  |  |  |
| DaGAN |  |  |  |  |  |
| LIA |  |  |  |  |  |

**Table 28: Visualization of processed faces in ScaleDF.**
| Name | Demo 0 | Demo 1 | Demo 2 | Demo 3 | Demo 4 |
| --- | --- | --- | --- | --- | --- |
| AMatrix |  |  |  |  |  |
| StyleMask |  |  |  |  |  |
| MRFA |  |  |  |  |  |
| HyperReenact |  |  |  |  |  |
| MCNet |  |  |  |  |  |
| CVTHead |  |  |  |  |  |
| FollowYourEmoji |  |  |  |  |  |
| LivePortrait |  |  |  |  |  |
| Megactor |  |  |  |  |  |
| G3FA |  |  |  |  |  |

**Table 29: Visualization of processed faces in ScaleDF.**
| Name | Demo 0 | Demo 1 | Demo 2 | Demo 3 | Demo 4 |
| --- | --- | --- | --- | --- | --- |
| FSRT |  |  |  |  |  |
| SkyReels-A1 |  |  |  |  |  |
| StyleGAN2 |  |  |  |  |  |
| VQGAN |  |  |  |  |  |
| StyleGAN3 |  |  |  |  |  |
| StyleGAN-XL |  |  |  |  |  |
| SD2.1 |  |  |  |  |  |
| SD1.5 |  |  |  |  |  |
| SDXL |  |  |  |  |  |
| PixArt-Alpha |  |  |  |  |  |

**Table 30: Visualization of processed faces in ScaleDF.**
| Name | Demo 0 | Demo 1 | Demo 2 | Demo 3 | Demo 4 |
| --- | --- | --- | --- | --- | --- |
| Midjourney |  |  |  |  |  |
| SD3.5 |  |  |  |  |  |
| FLUX.1 [dev] |  |  |  |  |  |
| CogView4 |  |  |  |  |  |
| CogView3 |  |  |  |  |  |
| Kolors |  |  |  |  |  |
| Hunyuan-DiT |  |  |  |  |  |
| LTX-Video |  |  |  |  |  |
| HunyuanVideo |  |  |  |  |  |
| Pika |  |  |  |  |  |

**Table 31: Visualization of processed faces in ScaleDF.**
| Name | Demo 0 | Demo 1 | Demo 2 | Demo 3 | Demo 4 |
| --- | --- | --- | --- | --- | --- |
| GPT-Image-1 |  |  |  |  |  |
| Janus-Pro |  |  |  |  |  |
| SimpleAR |  |  |  |  |  |
| Wan-T2V |  |  |  |  |  |
| Pyramid Flow |  |  |  |  |  |
| CogVideoX |  |  |  |  |  |
| SDEdit |  |  |  |  |  |
| E4E |  |  |  |  |  |
| EDICT |  |  |  |  |  |
| DiffusionCLIP |  |  |  |  |  |

**Table 32: Visualization of processed faces in ScaleDF.**
| Name | Demo 0 | Demo 1 | Demo 2 | Demo 3 | Demo 4 |
| --- | --- | --- | --- | --- | --- |
| VecGAN |  |  |  |  |  |
| InstructPix2Pix |  |  |  |  |  |
| IP-Adapter |  |  |  |  |  |
| MaskFaceGAN |  |  |  |  |  |
| SDFlow |  |  |  |  |  |
| EmoStyle |  |  |  |  |  |
| Triplane |  |  |  |  |  |
| FaceID |  |  |  |  |  |
| AnySD |  |  |  |  |  |
| MagicFace |  |  |  |  |  |

**Table 33: Visualization of processed faces in ScaleDF.**
| Name | Demo 0 | Demo 1 | Demo 2 | Demo 3 | Demo 4 |
| --- | --- | --- | --- | --- | --- |
| RigFace |  |  |  |  |  |
| FluxEdit |  |  |  |  |  |
| RFInversion |  |  |  |  |  |
| Step1X-Edit |  |  |  |  |  |
| MakeItTalk |  |  |  |  |  |
| Wav2Lip |  |  |  |  |  |
| Audio2Head |  |  |  |  |  |
| SadTalker |  |  |  |  |  |
| Video-Retalking |  |  |  |  |  |
| DreamTalk |  |  |  |  |  |

**Table 34: Visualization of processed faces in ScaleDF.**
| Name | Demo 0 | Demo 1 | Demo 2 | Demo 3 | Demo 4 |
| --- | --- | --- | --- | --- | --- |
| IP_LAP |  |  |  |  |  |
| Real3DPortrait |  |  |  |  |  |
| FLOAT |  |  |  |  |  |
| JoyVASA |  |  |  |  |  |
| DAWN |  |  |  |  |  |
| AniTalker |  |  |  |  |  |
| AniPortrait |  |  |  |  |  |
| EDTalk |  |  |  |  |  |
| Diff2Lip |  |  |  |  |  |
| JoyHallo |  |  |  |  |  |

**Table 35: Visualization of processed faces in ScaleDF.**
| Name | Demo 0 | Demo 1 | Demo 2 | Demo 3 | Demo 4 |
| --- | --- | --- | --- | --- | --- |
| Ditto |  |  |  |  |  |
| KDTalker |  |  |  |  |  |
| Echomimic |  |  |  |  |  |

**Table 36: Demonstration of the perturbations used when training models on the ScaleDF dataset.**
| Pert. | Elaboration | Demo 0 | Demo 1 | Demo 2 | Demo 3 |
| --- | --- | --- | --- | --- | --- |
| ResizeCrop | Randomly crop and resize an image to a specified size. |  |  |  |  |
| ColorJitter | Randomly<br>change the brightness, contrast, saturation, and hue of an image. |  |  |  |  |
| Blur | Randomly apply<br>a blur filter to an image. |  |  |  |  |
| Pixelate | Pixelate random portions of an image. |  |  |  |  |
| Rotate | Randomly rotate<br>an image within a given range of degrees. |  |  |  |  |
| GrayScale | Convert an image into grayscale. |  |  |  |  |
| Padding | Pad an image<br>with random colors, height and width. |  |  |  |  |
| AddNoise | Add random<br>noise to an image. |  |  |  |  |
| VertFlip | Flip an image<br>vertically. |  |  |  |  |
| HoriFlip | Flip an image<br>horizontally. |  |  |  |  |

**Table 37: Demonstration of the perturbations used when training models on the ScaleDF dataset.**
| Pert. | Elaboration | Demo 0 | Demo 1 | Demo 2 | Demo 3 |
| --- | --- | --- | --- | --- | --- |
| PerspChange | Randomly transform the perspective of an image. |  |  |  |  |
| ChangeChan | Randomly shift, swap, or invert the channel of an image. |  |  |  |  |
| EncQuality | Randomly<br>encode (reduce) the quality of an image. |  |  |  |  |
| Sharpen | Randomly<br>enhance the edge contrast of an image. |  |  |  |  |
| Skew | Randomly skew<br>an image by a certain angle. |  |  |  |  |
| ShufPixels | Randomly<br>rearrange (shuffle) the pixels within an image. |  |  |  |  |
| Xraylize | Simulate the effect of an X-ray on an image. |  |  |  |  |
| GlassEffect | Add glass effect onto an image with random extent. |  |  |  |  |
| OptDistort | View an image through a medium that randomly distorts the light. |  |  |  |  |
| Solarize | Invert all pixel values above a random threshold. |  |  |  |  |

**Table 38: Demonstration of the perturbations used when training models on the ScaleDF dataset.**
| Pert. | Elaboration | Demo 0 | Demo 1 | Demo 2 | Demo 3 |
| --- | --- | --- | --- | --- | --- |
| Zooming | Randomly<br>simulate the effect of zooming in or out. |  |  |  |  |
| Elastic | Simulate a jelly-like distortion of an image. |  |  |  |  |
| FancyPCA | Random use<br>PCA to alter the intensities of the RGB channels. |  |  |  |  |
| GridDistort | Apply random non-linear distortions within a grid to an image. |  |  |  |  |
| ISONoise | Apply random camera sensor noise to an image. |  |  |  |  |
| Multiple | A random value, is multiplied with the pixel values of an image. |  |  |  |  |
| Posterize | Randomly reduce the number of bits of each pixel. |  |  |  |  |
| Gamma | Alter the luminance values of an image by applying a power-law function. |  |  |  |  |
| Spatter | Randomly create a spatter effect on an image. |  |  |  |  |
| Binary | Use different<br>methods to binarize an image. |  |  |  |  |

## Peer Reviews

confidence: 4
contribution: 3
initial_rating: 4
presentation: 4
questions: The paper is well organized and presented, together with the proposed extensive dataset to study the scaling laws for deepfake detection problem as well as the corresponding valuable observations drawn from the experimental results. Nevertheless, there are some concerns which should be addressed in the rebuttal to further enhance the quality of the submission, including the discussion upon deepfake detection for general images, the influence made by processing operations, the experiments based on more existing detection methods, and the analysis among the generated data distribution from various method categories.
soundness: 3
strengths: + The proposed dataset for studying the scaling laws for deepfake detection is extensive, large-scale, and built with systematical structure (e.g. containing various categories of deepfake generation methods, in which these categories are balanced).
+ The observations of scaling laws drawn from the experiments provide valuable rule-of-thumb for the following researches upon deepfake detection, e.g. focusing more on the diversity of generation method categories, as well as the importance of data augmentation in the context of scaling.
summary: This paper proposes to study the scaling laws for deepfake detection, where a large scale dataset (named as ScaleDF) composed of 5.8M real face images from 51 different domains/datasets and 8.8M fake face images from 102 deepfake generation methods is built for supporting such study, in which the various perspectives of diversity are specifically considered. The study aims at the relation among deepfake detection model performance (in terms of AUC and EER) and the number of real image domains, deepfake generation methods, and training images, in which two main observations are drawn: 1) power-law scaling is found with respect to the number of training real domains and deepfake methods, but not the caused by a change in the number of real or fake images; 2) double-saturating power-law scaling is observed with respect to the number of training images.
weaknesses: - The current target of deepfake detection only focuses on human faces, while there lacks for the discussion upon general images, and it is hard to estimate if the drawn observations can be applied to general images. 
- As the current target of study is facial images, there exists data preprocessing pipeline particularly designed for faces (i.e. face detection, alignment, and cropping). Nevertheless, it is uncertain if the preprocessing operation (especially the alignment operation which has the transformation upon facial regions) would affect the investigation upon deepfake detection (the related question: why it is necessary to have the face frontalization to perform the deepfake detection).
- The current deepfake detection model used in this study is based on a ViT-based binary classifier built by the authors, it would be better to investigate if the same observations can be drawn while using any other existing deepfake detectors.  
- In addition to discover the scaling laws, there should be further analysis upon the dependencies among the data distributions from different domains (e.g. for detector which has been trained upon face reenactment images, the improvement brought by samples from face attribute editing would be less) thus indicating the potential shortfall/gap in the entire training data distribution which should be filled up in the coming future (i.e. the region in the data distribution which would be more beneficial/informative for learning the detector).

---

confidence: 4
contribution: 3
initial_rating: 4
presentation: 3
questions: 1.The authors may clarify whether any theoretical interpretation or modeling effort was made to explain the observed scaling exponents, beyond empirical curve fitting.
2.The paper should better distinguish deepfake detection from standard image classification in terms of scaling behavior and identify any conditions where the two might diverge.
3.The authors are encouraged to extend the scaling analysis to multimodal or temporal settings and discuss whether similar power-law behavior is expected in those scenarios.
soundness: 3
strengths: 1. The authors conduct a careful and systematic investigation of scaling laws for deepfake detection, varying three dimensions: the number of real domains, forgery methods, and image samples. Each scaling relationship is fit using well-defined power-law or double-saturating models, with high coefficients of determination (R² > 0.98). This rigorous quantitative approach is consistent with prior scaling studies such as Kaplan et al. (2020, OpenAI), showing a commendable level of experimental thoroughness and statistical discipline.

2. The proposed ScaleDF dataset (51 real domains, 102 forgery methods, and 14 million images) is an impressive engineering contribution that significantly exceeds prior benchmarks like ForgeryNet and DF40. This large-scale dataset substantially improves coverage across different generation paradigms (GANs, Diffusion, Flow Matching, etc.) and offers a strong foundation for future research on data-driven forensic generalization. Its scope alone makes it a valuable community resource even if the methodological contributions are limited.

3. The paper is well-written, with transparent documentation of the training settings, augmentation schemes, and evaluation protocols. The use of standard architectures and public baselines enhances reproducibility, and the authors provide enough implementation details to replicate their findings. Such openness aligns with reproducibility practices.
summary: This paper introduces ScaleDF, a large dataset for deepfake detection that includes 51 real domains, 102 forgery methods, and 14 million images. Using this dataset, the authors study how detection performance scales with the number of real domains, fake methods, and images, finding clear power-law trends. The study is careful and the dataset is useful, but it mostly confirms scaling behaviors already known from image classification, without adding new theory or model ideas.
weaknesses: 1. The study provides solid empirical results, but the theoretical novelty is quite limited. The observed scaling laws largely mirror patterns already well-documented in earlier literature. The functional forms, such as (1 - \text{AUC} = A N^{-\alpha}) and the double-saturating power law, directly follow existing analyses. The paper does not attempt to offer a new theoretical explanation for the scaling exponents or their transitions, so the contribution feels more like a domain-specific replication than a conceptual advance. While the authors present neat empirical curves, they stop short of explaining why these scaling behaviors emerge. 

2. Much of the contribution overlaps with what is already known from image classification scaling. Since deepfake detection here is essentially a binary image classification task trained on standard ViT architectures, it is unsurprising that the same scaling behaviors appear. This replication of known trends, similar to  prior conclusion reported for vision transformers, makes the findings somewhat expected. The paper would have been stronger if it explored conditions under which deepfake detection behaves differently from general image classification, or if it revealed forensic-specific scaling effects.

3. The scope is confined to static image detection and overlooks temporal or multimodal dynamics. All experiments focus on single-frame image analysis, leaving out the temporal and cross-modal cues that are central to practical deepfake forensics. It would be possible that multimodal scaling behaves differently and reveal richer dynamics. By excluding these aspects, the paper captures only part of the scaling landscape, reducing the generality of its conclusions for real-world multimodal scenarios.

---

confidence: 4
contribution: 2
initial_rating: 2
presentation: 3
questions: The paper presents several weaknesses, in my opinion, that are challenging to clarify and resolve under the current framework. I therefore do recommend the paper acceptance at this stage. Please refer to the Weaknesses for details.
soundness: 2
strengths: - The problem of deepfake detection is an important research topic given the current popularity and advances of deep generative models.

- The paper has a clear structure and makes it easy to follow.
summary: This paper presents an empirical study of scaling laws for the deepfake detection task. Specifically, the authors collect a large dataset consisting of over 5.8 real images and 8.8 million fake images from deepfake models, and formalize the task as a binary classification problem. The authors further test the performance using different deepfake detection methods on this proposed ScaleDF dataset, and propose several empirical observations of the scaling behaviors from those models, such as the power-law scaling w.r.t. the number of training real domains and deepfake methods.
weaknesses: - From a problem formulation perspective, while deepfake detection is a valuable research direction, the paper approaches it as a binary classification task in a somewhat oversimplified manner. In practice, current generative methods often perform fine-grained editing on only small regions of an image, which makes detection more nuanced. As a result, the paper’s formulation may limit its applicability to more realistic and challenging scenarios, narrowing its potential downstream impact, as an empirical analytical work.

- From the experimental perspective, the paper lacks comparison on several recent important detection methods proposed under the similar binary classification and scaled-up setting, such as [a].

- The paper could benefit from a more in-depth analysis. As it stands, the evaluation lacks detailed breakdowns and mostly presents high-level observations—such as “scaling helps”, which, while valid, are relatively intuitive and offer limited new insight. A more granular analysis, such as domain-wise or manipulation-type-specific performance, could yield more meaningful takeaways.

- Additionally, as the authors acknowledge, the dataset may raise ethical and copyright concerns due to its inclusion of a large number of deepfake face images. I would recommend that ethical reviewers examine this aspect closely.

- I would advise more caution regarding the “in-the-wild” setting described in Section 3.1. Benchmarks like Celeb-DF V2 and ForgeryNet may contain images that overlap with the training data in the proposed dataset, potentially inflating evaluation results. Further validation is needed to support the robustness of the in-the-wild claims.

---
[a] D^3: Scaling Up Deepfake Detection by Learning from Discrepancy. In CVPR 2025.

---

confidence: 4
contribution: 2
initial_rating: 4
presentation: 3
questions: Please see Weaknesses
soundness: 3
strengths: 1. The authors propose ScaleDF, the biggest deepfake dataset which contains 51 real domains and 102 deepfake methods, the dataset far exceeds the scale and diversity of prior deepfake detection datasets, addressing a critical bottleneck for robust experimental analysis. 

2. The authors conduct a thorough study of scaling laws in deepfake detection. It empirically validates power-law scaling for AUC with respect to the number of real domains and deepfake methods. 

3. The authors use clear mathematical formulations (e.g., $1-\bar{AUC}=A \cdot N^{-\alpha}$ for scaling laws), and provide extensive ablations and benchmarking to support their claims.
summary: In this paper, the authors conduct an empirical study of scaling laws for deepfake detection. To this end, the authors first construct ScaleDF, a dataset comprising over 5.8 million real face images from 51 domains and over 8.8 million fake images generated by 102 deepfake methods. Then, the authors investigate how model performance scales with the number of real domains, deepfake methods, and training images, observing power-law relationships akin to those in LLMs. 

Overall, this paper offers meaningful insights into the development of future deepfake detectors. Nevertheless, the analysis of scaling law phenomena is somewhat constrained, more comprehensive and diverse experimental investigations are needed.
weaknesses: 1.  The core contributions of this paper are largely around dataset compilation and large-scale experimentation rather than providing new algorithms, theoretical insights, or methodological innovations for deepfake detection. 

2. The experiments are mainly based on standard ViT backbones, without exploring whether alternate architectures, more nuanced objective functions, or specialized training regimes (e.g., domain adaptation or contrastive representation learning) might better leverage data diversity. 

3. The results generally present average AUC or EER. While this is appropriate for summarizing trends, it lacks fine-grained details, such as how scaling affects different categories of deepfake (e.g., face swapping vs. attribute editing). 

4. Domain leakage should be considered. Although some effort is made to avoid overfitting, at such a scale, there is inevitable dataset overlap across splits. The measures taken for “cross-domain” or “cross-method” testing are described, yet quantification of leakage is missing.

5. The scaling law is observed with empirical results. There is little attempt to model, explain, or theoretically motivate why these (double-)power laws arise in deepfake detection, i.e., no mechanistic or statistical argument is proposed to connect data diversity, method coverage, or architecture capacity. For instance, the choice of power law forms such as $1-\bar{AUC}=A \cdot N^{-\alpha}$ is motivated mainly by empirical fit. Moreover, some thresholds are left as extrapolations (e.g., “300 domains to reach 0.95 AUC”), without uncertainty quantification.