A Large-scale Universal Evaluation Benchmark For Face Forgery Detection

## Abstract

Abstract With the rapid development of AI-generated content (AIGC) technology, the production of realistic fake facial images and videos that deceive human visual perception has become possible. Consequently, various face forgery detection techniques have been proposed to identify such fake facial content. However, evaluating the effectiveness and generalizability of these detection techniques remains a significant challenge. To address this, we have constructed a large-scale evaluation benchmark called DeepFaceGen, aimed at quantitatively assessing the effectiveness of face forgery detection and facilitating the iterative development of forgery detection technology.
DeepFaceGen consists of 776 , 990 776 990 776,990 776 , 990 real face image/video samples and 773 , 812 773 812 773,812 773 , 812 face forgery image/video samples, generated using 34 34 34 34 mainstream face generation techniques. During the construction process, we carefully consider important factors such as content diversity, fairness across ethnicities, and availability of comprehensive labels, in order to ensure the versatility and convenience of DeepFaceGen. Subsequently, DeepFaceGen is employed in this study to evaluate and analyze the performance of 13 13 13 13 mainstream face forgery detection techniques from various perspectives.
Through extensive experimental analysis, we derive significant findings and propose potential directions for future research. The code and dataset for DeepFaceGen are available at https://github.com/HengruiLou/DeepFaceGen .

## 1 Introduction

In recent years, AIGC  [] technology has experienced rapid development, significantly enhancing its capabilities in abstract concept learning and content generation. This technology has initiated a global wave of artificial intelligence advancements, fundamentally transforming industries such as media, entertainment, e-commerce, and education.

However, AIGC is a double-edged sword that, while revolutionizing production manner, also introduces new security risks. Malicious individuals can use AIGC to forge and tamper with data, making it difficult to verify the authenticity of generated facial images and videos  []. This tampering complicates the pursuit of truth, erodes trust in multimedia information, and poses significant security threats to society.
As a result, criminal activities such as financial scams, internet rumors, and identity theft have become increasingly widespread.

To address the misuse of deepfake facial technology, numerous researchers from both industry and academia have proposed various techniques for detecting face deepfakes. These techniques heavily rely on publicly available face deepfake datasets. Thus, high-quality datasets are the cornerstone for developing effective deepfake detection techniques. Recently, several deepfake datasets have been created using deepfake techniques to assist researchers in training and evaluating their detection methods. However, most current deepfake datasets focus on relatively outdated localized editing based face forgery techniques.

Recently, OpenAI released DALL·E  [] and Sora  [], which capable of generating full images and videos from textual descriptions, sparking a wave of full-image generation. This technology surpasses the limitations of using existing images or videos for localized edits, adopting a generative approach to creating fake content. In quick succession, numerous outstanding AIGC products have emerged, achieving unprecedented levels of generative technology. While enhancing productivity and creative efficiency, these advancements also pose significant challenges for deepfake detection research.

Therefore, some researchers adopt the diffusion based generation technology  [] to build the image dataset for AIGC detection. Those datasets primarily consist of general images with only a subset containing facial data, which lack significant diversity and richness in terms of facial variations. In terms of video datasets, there is a notable lack of deepfake video datasets that incorporate full-image generation based face forgery techniques, which are crucial for advancing face deepfake detection research.
The absence of the evaluation dataset has led to a gap in face deepfake detection research, causing it to fall behind the rapid advancements in deepfake technology.

To address above challenge, this paper presents DeepFaceGen, a comprehensive and versatile evaluation benchmark specifically developed for face forgery detection. The main goal of DeepFaceGen is to facilitate the advancement of face forgery detection techniques. The benchmark encompasses a substantial dataset consisting of $463,583$ real images, $313,407$ real videos, $350,264$ forgery images, and $423,548$ forgery videos. The forgery samples are generated using 34 prevalent image/video generation techniques. Leveraging DeepFaceGen, we conduct a comprehensive evaluation of existing face forgery techniques, examining their performance across various aspects such as forgery manner, generation framework, and generalization ability. Through extensive experimentation, we uncover noteworthy insights that are anticipated to provide valuable guidance for face forgery detection tasks.

## 2 Related Works

In this section, we provide a comprehensive overview of the existing deepfake datasets, presenting detailed information summaries in Table 1. The survey of both face forgery technology and face forgery detection technology can be found in *Appendix A.1*.

Early face forgery detection datasets generally suffer from a limited variety of forgery methods and are constrained in both quantity and quality. UADFV  [] is the first dataset designed for face forgery detection. It only contains $49$ fake videos generated with the FakeApp application  []. The construction of APFDD  [], Celeb-DF  [], and DeeperForensics  [] has significantly increased the scale of face forgery detection datasets. However, these datasets still only contain a single forgery method. To enrich the variety of forgery techniques in datasets, Korshunov *et al*. [] developed DeepfakeTIMIT using two face swapping techniques. Subsequently, Rossler *et al*.  [] created FF++ using a total of four forgery methods: Deepfake  [], Face2face  [], Faceswap  [], and NeuralTextures  []. However, the size and diversity of FF++ are still insufficient, making it challenging to optimally train high-performance deep models with a large number of parameters. Zi *et al*.  [] collected deepfake samples from the internet to create WildDeepfake, which includes facial motion sequences extracted from videos. After manually removing videos without corresponding real faces, the number of fake videos stands at $3,509$. Although the visual effects are closer to real-life scenarios, the limited data volume poses constraints on training high-performance deep models.

To address the issues of poor generation quality and coarse tampering traces in early face forgery detection datasets, DFDC  [], initially released as part of Facebook’s eponymous competition, contains $5,250$ videos, which was later supplemented to reach $104,500$ fake videos generated using eight different methods to ensure dataset diversity. Kwon *et al*.  [] generated the KoDF dataset, comprising $175,776$ fake videos using $6$ forgery methods. Most videos in KoDF feature Korean individuals, representing the first effort to address the underrepresentation of Asian populations in existing forgery detection datasets. FakeAVCeleb  [] consists of $19,500$ fake videos generated using $3$ face forgery methods: Faceswap, DeepFaceLab  [], and FSGAN  []. He et al.  [] developed ForgeryNet, the first face forgery detection dataset that includes videos and images. They used $15$ deepfake methods to generate $121,617$ fake videos and $1,457,861$ fake images. Although the forgery quantity and forgery method have been significantly improved, ForgeryNet’s forgery method is still limited to the face forgery technology based on localized editing, which cannot provide data support for detecting the novel AIGC forgery method based on full-image generation. Additionally, ForgeryNet fails to take into account the aspects of racial fairness and fine-grained annotations, which are essential for conducting a comprehensive and multi-perspective evaluation.

The rapid development of full-image generation based face forgery techniques, exemplified by diffusion models, has led to the emergence of outstanding AIGC products such as Sora  [], Midjourney  [] and DALL·E  []. These products have significantly impacted the field with their astonishing realism. The construction of deepfake datasets based on full-image generation techniques has become increasingly urgent due to the astonishing realism of these AIGC products. IEEE VIP Cup  [] and DE-FAKE  [] are among the first to contain diffusion models for generating images, pioneering efforts in this area. Additionally, IEEE VIP Cup includes localized editing based forgery techniques in its dataset construction, marking the first effort to build a general-purpose dataset in the field of image forgery datasets. Based on the CIFAR-10 dataset  [], J. Bird *et al*.  [] created the CiFAKE dataset by generating fake images using SDV1.4. GenImage  [] includes 7 diffusion-based full-image generation forgeries and the localized editing forgery technology BigGAN  [], covering $1,350,000$ images across $1,000$ categories. In the context of the proliferation of full-image generation forgeries, these datasets provide valuable image evaluation data for researchers. However, the quantity and quality of facial data in these datasets are difficult to guarantee. They do not provide dedicated data for deepfake detection research on facial images and lack datasets that include full-image generated facial forgeries within face video forgery datasets.

**Table 1: Summary of existing deepfake datasets.**
| Dataset Name | Content | Forged Data | Generation Manner | Racial | Fine-grained | Forgery | Public |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Image | Video | Localized | Full-image | Balance | Annotation | Approaches | Avalibility |  |  |
| APFDD [] | Face | 5,000 | - | $\checkmark$ | $\times$ | $\times$ | $\times$ | 1 | $\times$ |
| DeepArt [] | Art | 73,411 | - | $\times$ | $\checkmark$ | $\times$ | $\times$ | 5 | $\checkmark$ |
| IEEE VIP Cup [] | General | 7,000 | - | $\checkmark$ | $\checkmark$ | $\times$ | $\times$ | 14 | $\times$ |
| DE-FAKE [] | General | 60,000 | - | $\times$ | $\checkmark$ | $\times$ | $\times$ | 4 | $\times$ |
| CiFAKE [] | General | 60,000 | - | $\times$ | $\checkmark$ | $\times$ | $\times$ | 1 | $\checkmark$ |
| GenImage [] | General | 1,350,000 | - | $\checkmark$ | $\checkmark$ | $\times$ | $\times$ | 8 | $\checkmark$ |
| UADFV [] | Face | - | 49 | $\checkmark$ | $\times$ | $\times$ | $\times$ | 1 | $\times$ |
| DeepfakeTIMT [] | Face | - | 320 | $\checkmark$ | $\times$ | $\times$ | $\times$ | 2 | $\checkmark$ |
| FF++ [] | Face | - | 4,000 | $\checkmark$ | $\times$ | $\times$ | $\checkmark$ | 4 | $\checkmark$ |
| Celeb-DF [] | Face | - | 5,639 | $\checkmark$ | $\times$ | $\times$ | $\checkmark$ | 1 | $\checkmark$ |
| DeeperForensics [] | Face | - | 10,000 | $\checkmark$ | $\times$ | $\times$ | $\times$ | 1 | $\checkmark$ |
| WildDeepfake [] | Face | - | 3,509 | $\checkmark$ | $\times$ | $\times$ | $\times$ | unknown | $\checkmark$ |
| DFDC [] | Face | - | 104,500 | $\checkmark$ | $\times$ | $\times$ | $\times$ | 8 | $\checkmark$ |
| KoDF [] | Face | - | 175,776 | $\checkmark$ | $\times$ | $\times$ | $\times$ | 6 | $\times$ |
| FakeAVCeleb [] | Face | - | 19,500 | $\checkmark$ | $\times$ | $\times$ | $\times$ | 3 | $\checkmark$ |
| ForgeryNet [] | Face | 1,457,861 | 121,617 | $\checkmark$ | $\times$ | $\times$ | $\times$ | 15 | $\checkmark$ |
| DeepFaceGen (ours) | Face | 350,264 | 423,548 | $\checkmark$ | $\checkmark$ | $\checkmark$ | $\checkmark$ | 34 | $\checkmark$ |

## 3 Evaluation Dataset Construction

In this section, we aim to construct a robust and extensive benchmark for the detection of face forgery. To accomplish this, we carefully consider a range of critical factors including the manner of generation, generation framework, content diversity, ethnic fairness, and label richness throughout the benchmark development process. Following this, we provide a detailed introduction to the methodologies employed for collecting and generating forged samples. Additionally, we introduce the authentic data sources utilized by DeepFaceGen.
Lastly, we present a comprehensive summary of the detailed data information encompassed within DeepFaceGen.

To enhance the diversity of DeepFaceGen, we augment its dataset by incorporating a selection of pre-existing forged face samples alongside newly generated ones using popular image and video generation techniques. These collected samples adhere to the principle of ethnic fairness. Specifically, from references  [] and  [], we choose samples created through localized editing techniques such as face swapping, face reenactment, and face alteration. Detailed information about these collected samples can be found in *Appendix A.2*.

### 3.1 Forged Face Sample Generation

For the novel AIGC techniques, we employ a set of $17$ prevalent full-image generation based face forgery techniques. Additionally, we incorporate $17$ classical localized editing based face forgery techniques, excluding the new generation methods. In the following section, we extensively elaborate on the generation processes for both categories of techniques.

Full-image Based Generation techniques utilize text or image input to generate full-image samples. The design of the prompt plays a crucial role in determining the quality of the generation outcome. Hence, we primarily present the process of prompt construction, followed by the description of forgery methods.

- •
Prompts Construction.
In the design of prompts, we strive to achieve both content diversity and fairness, which are accompanied by a strong emphasis on detailed prompt descriptions. For each prompt, we establish fundamental attributes, such as age, gender, and skin tone, while also providing comprehensive specifications regarding the person’s background and physical features. The inclusion of these extensive textual attribute details further facilitates the evaluation of forgery detection performance at a fine-grained level. A total of $9$ textual attributes are defined in the prompt construction process. By exhaustively generating prompts using all possible combinations of these textual attributes, we ensure the creation of a diverse and equitable set of forged data. For further elaboration on these prompts, please refer to *Appendix A.3*.
- •
Text2Image generation techniques involve three main categories: GAN, autoregressive, and diffusion frameworks. Some of these techniques have been developed into commercial products. In order to enhance the practicality and universality of DeepFaceGen, we have incorporated mature commercial products and popular open-source methods to generate the forgery samples.
For GAN-based models, we have adopted the popular open-source DF-GAN  [] which employs adversarial training between the generator and discriminator to achieve impressive image generation capabilities. As for autoregressive based models, we have utilized OpenAI’s commercial product DALL·E and DALL·E 3 [], which treats text tokens and image tokens as a unified data sequence and uses a Transformer for auto-regression.
Given that existing high-quality generation techniques mostly rely on diffusion framework, we have incorporated specific models such as OpenAI’s Midjourney [], Baidu’s Wenxin  [], Stability.ai’s series products {Stable Diffusion 1 (SD1), Stable Diffusion 2 (SD2), Stable Diffusion XL (SDXL)}  [], and PromptHero’s open-source version of Midjourney (Openjourney, OJ)  []. To ensure that these models are suitable for face generation, we fine-tune pre-trained open-source text2video models using LoRA [].
- •
Image2Image generation involves utilizing an image as input to generate full-image samples, typically employing diffusion frameworks. Therefore, we utilize Stable Diffusion XL Refiner (SDXLR), Stable Diffusion InstructPix2Pix (Pix2Pix), and Stable Diffusion ImageVariation (VD)  [], all of which have achieved high rankings on Huggingface’s download charts.
- •
Text2Video techniques involve using a text prompt as input to generate a complete video sample, also relying on diffusion frameworks. However, due to unavailability of certain mature commercial products’ API, we have selected alternative products. Specifically, we have chosen MagicTime  [], AnimateDiff-Lightning (AnimateDiff)  [], AnimateLCM  [], Hotshot  [], and Zeroscope  [].

Localized Editing Based Generation technique generates forged samples by modifying certain parts of input face images. Existing Localized Editing techniques can be categorized into three types: face swapping, face reenactment, and face alteration.

- •
Face Swapping technique involves creating a manipulated face sample by exchanging the faces of two given image samples. In this study, we employ $8$ commonly used face swapping methods, namely FaceShifter  [], FSGAN  [], DeepFake  [], BlendFace  [], MMReplacement  [], DeepFakes-StarGAN-Stack (DSS), StarGAN-BlendFace-Stack (SBS), and SimSwap  []. Among these approaches, DSS and SBS are categorized as mixed face forgery methods, wherein the face alteration technique is initially applied before face swapping is performed.
- •
Face Reenactment technique involves transferring the facial movements and expressions from one person onto the face of another person. In this study, we utilize four specific approaches for face reenactment: Talking Head Video  [], ATVG-Net  [], FOMM  [], and Motion-cos  [].
- •
Face Alteration technique involves creating forged images by making subtle modifications to facial attributes such as hair color, beard, and glasses. The face alteration approaches utilized in this study include StyleGAN2  [], MaskGAN  [], StarGAN2  [], SC-FEGAN  [], and DiscoFaceGAN  [].

### 3.2 Authentic Face sample Collection

In order to ensure content diversity and ethnic fairness in the authentic face samples used in DeepFaceGen, we obtained real samples from reputable sources including  [],  [],  [], and  []. The final collection consists of $463,583$ images and $313,407$ videos, encompassing diverse races, genders, ages, expressions, hairs, backgrounds, and so on.
Please refer to *Appendix A.2* for more details.

### 3.3 Dataset Summarization

The aforementioned generation and collection processes yield the initial dataset samples. To ensure both sample quality and racial balance, postprocess operations are implemented to filter these samples. The SkinToneClassifier  [] is utilized for racial balance, ensuring skin tone balance in the generation and collection of localized editing-based and Image2Image face forgery methods. For full-image generation-based face forgery techniques (Text2Image and Text2Video), the combination and design of text prompts also take skin tone balance into consideration. Additionally, we employ YOLO  [] with manual screening to eliminate low-quality data. These measures effectively maintain the fairness and reliability of DeepFaceGen, resulting in the collection of $350,264$ forged images and $423,548$ forged videos. For a detailed breakdown of the sample numbers for different generation techniques, please refer to Figure 2 provided in *Appendix A.2*.

## 4 Benchmark Evaluation and Analysis

In this section, we employ DeepFaceGen to evaluate $13$ prevalent face forgery detection methods from various perspectives, such as generation approach type, generalization capability, and technique relevance. Subsequently, we analyze extensive experimental results and summarize key findings, elucidating the strengths and weaknesses of current face forgery detection techniques, as well as identifying potential directions for future research.

Evaluation Settings.
Based on the distinction in modality between images and videos, we partition DeepFaceGen into two parts. The image and video datasets are divided into training, validation, and test subsets in a ratio approximately $7:1:2$. To ensure fairness in evaluation, each subset maintains a ratio of real to fake instances close to $1:1$. For image-level assessments, we employ Xception  [], EfficientNet-B0  [], F3-Net  [], RECCE  [], DNANet  [], and FreqNet  []. For video-level evaluations, we select MesoNet  [], EfficientNet-B0  [], Xception  [], F3-Net  [], CViT  [], SLADD  [], and Exposing  [], as they exhibit exceptional performance in forgery video detection. The experiments are conducted separately on a machine equipped with an Nvidia A40 GPU (48GB VRAM) and two machines, each featuring a GeForce RTX 4090 GPU (24GB VRAM). More evaluation details are given in the *Appendix A.4*.

### 4.1 Evaluation of Mainstream Forgery Detection Techniques

In this section, we initiate the training of all forgery detection models utilizing training samples obtained from DeepFaceGen. We subsequently present and analyze the experimental results comprehensively, considering various aspects such as the sample modality, forgery technique, forgery technique type, and the framework employed by the forgery detection models.

#### 4.1.1 Image-level Evaluation and Analysis

Figure: Figure 1: Image-level Performance comparison of different forgery detection techniques. (a) Average detection performance ranking. (b) Detection performance for different generation techniques. (c) Detection performance for different generation manners and frameworks (marked with ①-⑧).
Refer to caption: extracted/5666211/fig5.jpg

Forgery Detection Technique Comparison.
Figure 1 (a) illustrates the average detection performance of various forgery detection techniques. It is evident from the figure that RECCE [] and DNANet [] outperform the rest, while Xception [] and F3-Net [] exhibit poor performance. RECCE utilizes an Encoder-Decoder structure for forgery detection, while DNANet [] combines an encoder classifier and a contrastive learning projector. These two methods leverage their respective architectures to extract detailed features related to the forgery. FreqNet [], on the other hand, extracts high-frequency features across both spatial and channel dimensions, capturing subtle forgery variations. In contrast, general-purpose classifiers like Xception [], F3-Net [], and EfficientNet-B0 [], which employ convolutional encoder architectures, do not perform as well as the specialized methods designed for face forgery detection. Therefore, it can be inferred that * the details extraction module plays a critical role in the detection of face image forgery (Finding 1)*.

Generation Manner and Framework.
Based on Figure 1 (c), it is evident that localized editing techniques (face swapping, face reenactment, face alteration) for image generation can produce more challenging identification samples compared to full-image generation techniques (Text2Image and Image2Image). This can be attributed to *the relative ease of generating authentic images by modifying smaller localized areas rather than the entire image (Finding 2)*. However, further research is required to enhance the performance of full-image generation techniques.
Regarding different generation frameworks, it is evident that autoregressive based techniques (DALL·E and DALL·E3  []) achieve the highest quality of forgery, surpassing diffusion-based and GAN-based techniques. The newly proposed diffusion-based framework demonstrates the second-best average performance, indicating its potential for further development. Conversely, GAN-based generation techniques exhibit the poorest quality for forgery. Therefore, it can be concluded that *autoregressive-based and diffusion-based generation techniques are capable of producing more realistic forged face images than GAN-based generation techniques (Finding 3)*.

Input Modality.
Based on the results depicted in Figure 1 (b)(c), it is apparent that both Text2Image (Midjourney [], OJ [], SD1 [], SD2 [] and SDXL []) and Image2Image techniques (SDXLR [], Pix2Pix [], and VD []) that employ the same diffusion-based framework deliver comparable performance. Consequently, it can be deduced that *the choice of input modality has minimal influence on the quality of image generation (Finding 4)*.

Figure: Figure 2: Video-level Performance comparison of different forgery detection techniques. (a) Average detection performance histogram. (b) Detection performance for different generation techniques. (c) Detection performance for different generation manners and frameworks (marked with ①-③).
Refer to caption: extracted/5666211/Fig6.jpg

#### 4.1.2 Video-level Evaluation and Analysis

Forgery Detection Technique Comparison.
Figure 2 (a) depicts the average detection performance of various video forgery detection techniques. It is evident that both Exposing [] and SLADD [] outperform the other techniques. Exposing [] adopts a two-step approach: extracting frame-level facial bounding boxes from raw videos and subsequently extracting multiple disentangled local features from different regions for forgery detection. SLADD [] employs adversarial self-supervised training to identify various forgery detail features, which contributes to its outstanding performance. In contrast, general-purpose classifiers such as EfficientNet-B0 [], Xception [], F3-Net [], and CViT [] exhibit poor identification performance due to their lack of forgery detail information.
Thus, we can conclude that the *extraction of detailed features also plays a critical role in detecting face video forgery (Finding 5).*

Generation Manner and Framework.
This study focuses on high-quality full-image video generation techniques and predominantly adopts the diffusion-based framework. Methods [, , ] with poor visual video generation quality are not included in this investigation. Analysis of Figure 2 (b) reveals that full-image based generation techniques with diffusion framework demonstrate similar performance. Consequently, we can infer that *existing diffusion-based generation techniques possess a comparable ability to generate forged videos (Finding 6)*. Additionally, diffusion-based techniques exhibit lower performance compared to alternative methods, with face swapping yielding the best results. The potential explanation for this finding is that diffusion-based techniques, face reenactment, and face swapping alter the content of the full image, facial movements, and facial contour, respectively. Consequently, it can be inferred that *altering fewer aspects of the content leads to the generation of more authentic videos (Finding 7)*.

Figure: Figure 3: The cross-generalization ability verification matrices for image-level (a) and video-level (b) datasets. The training and testing samples, generated by various forgery techniques, are represented on the vertical and horizontal axes. The denotation for each number is provided in the *Appendix A.5*.
Refer to caption: extracted/5666211/heatmap5.jpg

### 4.2 Generalization Ability Evaluation to Different Forgery Techniques

In this section, we verify the cross-generalization ability among sub-datasets created using various forgery techniques. The results for image-level and video-level datasets, obtained using the Xception model for forgery detection, are presented in Figure  3. Furthermore, additional cross-generalization verification experiments with another $11$ forgery detection models can be found in *Appendix A.5*.

Generalization Ability Across Different Forgery Techniques. Figure  3 demonstrates that models trained on localized forgery images/videos exhibit superior generalization capability than models trained on full-image forgery images/videos. This difference can be attributed to several factors. Localized forgery techniques concentrate on specific facial regions, such as eyes, mouth, and skin texture, which also serve as vital clues for detecting full-image forgery images/videos. Conversely, full-image forgery methods consider the entire image, incorporating elements like background, lighting, and environment, which introduce significant variability across different datasets. Consequently, the model’s ability to generalize on localized editing samples is diminished. Thus, we can conclude that *Face forgery detection methods trained on localized editing samples generally demonstrate higher generalization capability compared to those trained on full-image generation samples (Finding 8)*.
Furthermore, from Figure  3(a), it is evident that the forgery detection technique trained and tested on samples generated by the full-image DF-GAN [] exhibits poor and good generalization ability, respectively. This finding further confirms *Finding 3* that full-image generation using GAN-based techniques results in low image quality, making it easily detectable by forgery detection techniques.

Internal Generalization Ability Analysis.
Figure  3 indicates that models trained on full-image forgery samples (Image2Image, Text2Image, and Text2Video) possess a high degree of internal generalization ability. This can be attributed to the significant similarities shared among samples generated by full-image generation techniques. Similarly, models trained on localized forgery images and videos demonstrate high and moderate internal generalization ability, respectively. Moreover, models trained on face reenactment videos and face swapping forgery videos exhibit a moderate level of generalization ability to each other. The findings imply a trend in forgery detection methods, where generalized forgery features are learned from images, while more specific forgery features are acquired from videos. This disparity may be attributed to the presence of redundant features in videos compared to single images. Hence, we can conclude that *models trained on full-image forgery images, localized forgery images, and full-image forgery videos display high internal generalization ability, whereas localized forgery videos do not (Finding 9).*

Figure: Figure 4: The forgery feature visualization for different forgey techniques on image-level (a) and video-level (b) datasets with t-SNE [].
Refer to caption: extracted/5666211/tsne3.jpg

### 4.3 Visualization Analysis of Forgery Detection Features

In this section, we utilize the fully connected layer features of the forgery detection model ResNet50 [] to visually evaluate the similarities among different forgery techniques. As illustrated in Figure 4, a clear distinction is observed in the feature space between full-image forgery samples (Image2Image, Text2Image, and Text2Video) and localized editing forgery samples (face alteration, face reenactment, and face swapping). This indicates that *the forgery features of full-image forgery samples and localized editing forgery samples are distinct (Finding 10).* It further confirms the *Finding 8&9*. Further analysis is given in *Appendix A.6*.

## 5 Conclusion

In this study, we present DeepFaceGen, the first comprehensive deep face forgery dataset that encompasses both localized editing and full-image generation samples. This dataset addresses the existing gap in large-scale general face forgery datasets. DeepFaceGen contains an extensive collection of over $350,000$ images and $400,000$ videos. We provide a detailed description of the dataset construction process and evaluate the performance of $13$ mainstream forgery detection techniques on samples forged using $34$ different generation techniques. By analyzing the results of these extensive experiments, we draw important findings that present novel perspectives and directions for the development of face generation and forgery detection techniques. We anticipate that this benchmark will have a far-reaching positive impact on the emerging field of artificial intelligence.

Challenge and Future Work.
Based on extensive experimentation and analysis, it is evident that current forgery detection techniques suffer from drawbacks, such as low identification accuracy, poor generalization ability, and a restricted range of forgery detection types. Moreover, the rapid development of face generation techniques has created a significant discrepancy, resulting in a lag in face forgery detection. In order to address this issue, the development of a self-evolving forgery detection framework is crucial to ensure that forgery detection techniques can keep up with the advancements in face generation techniques. Additionally, this paper presents a comprehensive evaluation benchmark comprising diverse content samples, various races, and fine-grained labeling. The design of objective and comprehensive quantification metrics, as well as the establishment of a complete pipeline, are crucial for future research. Further analysis regarding challenges and future directions can be found in the *Appendix A.7*.

## Appendix A Appendix

In the appendix, we provide survey of face forgery technology and face forgery detection technologies ( A.1), comprehensive statistical analysis of the DeepFaceGen dataset ( A.2), and the detailed descriptions of prompts construction ( A.3). We also outline the evaluation setting details ( A.4), details for generalization ability verification experiments of different ( A.5), and fine-grained analysis of forgery detection feature ( A.6). Additionally, we give detailed challenge discussions and future directions ( A.7), fine-grained attribute statistic analysis for different forgery techniques ( A.8), and potential negative social impacts ( A.9).

### A.1 Survey of Face Forgery Technology and Face Forgery Detection Technology

In this section, we present a comprehensive overview of both face forgery technologies and face forgery detection technologies. Regarding the former, we categorize face forgery methods into localized editing and full-image generation techniques based on their image/video generation approach. Subsequently, we discuss the forgery detection techniques designed specifically for these two types of forgery methods.

#### A.1.1 Localized Editing Based Face Forgery Technology

Localized editing based face forgery involves modifying specific facial features, such as expressions and movements. Traditional facial Photoshop (PS) techniques, which involve manual image manipulation, also fall within this scope. However, traditional PS techniques often leave detectable traces that can be identified by the naked eye. Therefore, survey of localized editing based face forgery focus on advanced deepfake methods including face swapping, face reenactment, and face alteration.

Face Swapping. Face swapping involves transferring the facial identity from a source image to a target image while preserving the expressions, movements, and background of the target image. Early face swapping techniques primarily relied on autoencoders. One such tool, Deepfake [], popularized by Reddit users, trains the facial images of the source and target persons separately, allowing the decoder to accurately reproduce their faces. In face swapping, the encoder extracts the source person’s facial features and inserts them into the target person’s image using the decoder. Shaoanlu [] introduces FaceswapGAN, which employs a face swapping attention mechanism to enhance image realism. This method also addresses occlusion issues using segmentation masks. RSGAN [] is designed for face swapping using two autoencoders to represent the hair and face regions. It replaces the face’s latent representation and reconstructs the image, effectively addressing issues such as mismatched face orientation and lighting. Nirkin *et al*. [] introduces FSGAN, which uses RNN-based methods to transfer expressions and movements from the target face to the source face. FSGAN demonstrates good generalization and requires fewer training samples. Li *et al*. [] introduces Faceshifter, a two-stage face-swapping method. It uses adaptive attention denormalization (AAD) for feature integration and employs a heuristic error acknowledgment refinement network (HEAR-Net) to address occlusion issues. Chen *et al*. [] introduces an identity injection module to eliminate identity constraints, and enhances the loss function with weak feature matching loss to improve face synthesis quality.

Face Reenactment. Face reenactment preserves the target image’s facial identity while replicating expressions, facial orientation, and body movements from the source image. Wang *et al*. [] introduces Imaginator, which uses a spatiotemporal feature fusion mechanism to decode continuous video from spatial features and motion. They employ two discriminators: one to evaluate the realism of facial appearances and the other to assess the realism of motions. Siarohin *et al*. [] introduces Monkey-Net, which separates appearance and motion information in images, enabling motion-driven animation. Monkey-Net includes a motion transfer network, an unsupervised keypoint detector, and a motion prediction network. It predicts the visual flow map for each keypoint by distinguishing keypoints in target and source images, thereby generating forged images. Siarohin *et al*. [] improves on Monkey-Net by introducing local affine transformations around keypoints, which better reproduce large pose variations. Pumarola *et al*. [] uses action unit annotations combined with unsupervised training and attention mechanisms to enhance model robustness. Tripathy *et al*. [] uses action units to represent facial expressions, processing the face and background separately to improve image quality and reduce identity information leakage. CycleGAN [] is widely used in face reenactment due to its flexible training capabilities between source and target domains. Xu *et al*. [] proposes a full-image reenactment method based on CycleGAN, which uses various receptive field specifications and PatchGAN to enhance image quality. Bansal *et al*. [] uses CycleGAN for data-driven, unsupervised video retargeting, effectively transferring continuous information for expression-driven animation. Wu *et al*. [] introduces ReenactGAN, which extracts facial contours using an encoder and maps them via CycleGAN. A pix2pix generator then reconstructs the image. This method uses only feedforward neural networks, enabling real-time expression reenactment.

Face Alteration. Face alteration modifies specific attributes like hair color, gender, and glasses without altering facial identity. Most face alteration techniques use GAN structures. The StyleGAN series [, , ] are notable for editing facial features, while StarGAN [] and StarGANV2 [] enable transformations across multiple image domains, offering better scalability. Another notable method is GANnotation [], which contains a triple continuity loss function for GAN-based face alteration and a direct facial expression alteration synthesis method. Kim *et al*. [] introduces a CAM consistency loss function based on CycleGAN’s cycle consistency loss function, which helps retain feature-independent positional information and can be applied to models like StarGAN. To address scalability and diversity issues in face alteration, Li *et al*. [] introduces hierarchical style disentanglement(HiSD), a hierarchical model that represents facial features as labels and attributes. Using an unsupervised approach, HiSD decouples these features, allowing for more precise modifications of target attributes.

#### A.1.2 Full-image Generation Based Face Forgery Technology

Based on the differences in network architecture, end-to-end full-image generation face forgery techniques can be categorized into gan-based models, autoregressive-based models, and diffusion-based models.

Figure: Figure 5: Full-image generation methods/products (above the timeline) and forgery detection techniques (below the timeline) are shown on a chronological timeline. GAN, Autoregressive, and Diffusion are marked with blue, orange, and black fonts, respectively.
Refer to caption: extracted/5666211/fig1.jpg

GAN-based Models. Based on their model structure, GANs can be classified into single-stage generation networks and stacked architectures. DF-GAN [], a single-stage generation network, uses one generator, one discriminator, and a pre-trained text encoder. It maps text to images by incorporating affine transformations, enabling direct image synthesis from textual descriptions. GoGAN [], a stacked architecture, generates higher resolution images in stages. Each branch’s generator captures the image distribution, while the discriminator assesses authenticity, refining image resolution and achieving stable training results. Despite their capabilities, GANs face stability issues and mode collapse. These limitations have led to their gradual replacement by autoregressive and diffusion models, which offer improved stability and better handling of diverse data distributions.

Autoregressive-based Models. Autoregressive-based models generate images by modeling spatial relationships between pixels and high-level attributes using an Encoder-Decoder architecture with a multi-head self-attention mechanism. In Text2Image generation, these models convert text and images into token sequences. The autoregressive model predicts image sequences from these tokens, which are then decoded into final images using techniques such as Variational Autoencoders (VAEs) to enhance image quality. Autoregressive models offer explicit density modeling and stable training compared to GANs. Notable examples include DALL·E [], which generates creative images from text prompts, CogView [], known for its high-quality image synthesis, and Make-A-Scene [], which enables interactive image generation. However, autoregressive models face limitations in computational resources, data requirements, and training time due to their large number of parameters. Diffusion models, which offer improved efficiency and require less data, have led to a decline in interest in autoregressive models.

Diffusion-based Models. Diffusion-based models have become the state-of-the-art in deep generative models, surpassing previous image and video synthesis techniques. Diffusion models generate images and videos by combining noise prediction models with conditional diffusion or classifier guidance. This process allows the diffusion model to create the desired output based on the provided guidance. These models excel at handling various input conditions and mitigating mode collapse, making them dominant in fields such as Text2Image, Image2Image, Text2Video, and Image2Video synthesis. Notable examples include GLIDE [], known for its high-quality Text2Image generation; Imagen [], which excels in photorealistic image synthesis; Sora [], a state-of-the-art Text2Video model; and Stable Diffusion [], which is widely used for its versatility and stability.

#### A.1.3 Detection Technique for Localized Editing Based Face Forgery

Detection techniques target localized editing based face forgeries by identifying artifacts left in various feature spaces during the forgery process. These techniques can be categorized into spatial domain-based, frequency domain-based, and temporal domain-based detection technique.

Spatial Domain-based Detection Technique. Zhao *et al*. [] suggests that the key to distinguishing real from forged faces lies in subtle local details. They propose a texture enhancement module, an attention generation module, and a bi-linear attention pooling module to help the model focus on facial texture details. However, these methods often overfit to specific forgery artifacts, leading to a rapid decline in detection performance when faced with unseen forgery methods. To avoid overfitting, researchers have generated forged faces by applying certain operations to real faces. Li *et al*. [] introduces the FaceX-Ray model, which detects forgery by identifying face fusion boundaries. During training, the model predicts image authenticity and performs pixel-wise classification on the gray scale map of fusion boundaries. This method does not rely on specific forgery artifacts, showing remarkable generalization capabilities in detecting forgeries from unseen methods. Shiohara *et al*. [] argues that forgeries often contain general forgery traces. They propose Self-Blended Images (SBI), synthetic forgeries created by transforming key points within the same face image, which show strong generalization against unknown forgery methods. However, this method performs poorly against full-face synthesis methods due to its reliance on the self-forgery process. Cao *et al*. [] introduces RECCE, combining reconstruction learning and classification to help the model learn compact features of real faces and uncover essential differences between real and fake faces. Some studies have explored the interpretability of deep face forgery detection models. Dong *et al*. [] hypothesizes that detection models identify authenticity by discerning information unrelated to facial identity. They use facial identity as an auxiliary label and designed source feature encoders and target encoders for identity recognition tasks.

Frequency Domain-based Detection Technique. Videos and images disseminated across online streaming media often undergo multiple compressions, resulting in low-quality images that obscure forgery artifacts. To address this issue, researchers have explored detection clues in the frequency domain. For instance, Qian *et al*. [] finds that forgery artifacts can be effectively extracted in the frequency domain. They design a frequency-aware decomposition module to adaptively capture forgery clues within images. Additionally, they introduce a local frequency information statistics module to gather frequency information from each local region of an image and recombine these statistics into multi-channel feature maps for the frequency domain. Since artifacts appear in different regions of various images, Wang *et al*. [] introduces a multi-modal and multi-scale autoregressive model (M2TR) to detect local artifact details at different spatial levels. This model incorporates frequency domain features as auxiliary information, enhancing its capability to detect forgeries in highly compressed images. While frequency domain-based methods show strong forgery detection capabilities in highly compressed images, their performance significantly declines when encountering unknown forgery methods.

Temporal Domain-based Detection Technique. Temporal domain forgery detection focuses on identifying dynamic inconsistencies between video frames over time. Masi *et al*. [] proposes a dual-stream branch network. One branch extracts dynamic temporal inconsistencies from consecutive video frames, and the other amplifies artifact details using a Laplacian of Gaussian (LoG) operator. Recognizing the correlation between forgery and anomaly detection tasks, Ruff *et al*. [] introduces the deep support vector data description (Deep SVDD) loss function to improve the intra-class compactness of real faces and the inter-class distinction between real and forged faces, enhancing the model’s generalization capability. Zheng *et al*. [] finds that setting the temporal convolution kernel size to 1 in 3D convolutional kernels enhances the network’s ability to capture temporal inconsistencies in forged videos. However, temporal inconsistencies can be compromised by noise, compression, and other factors, leading to reduced robustness in these methods.

#### A.1.4 Detection Technique for Full-image Generation Based Face Forgery

Research achievements in the detection of full-image generation based face forgery are currently limited. Researchers are attempting to break through the mindset of searching for clues specific to localized editing based face forgery and instead seek the unique fingerprints produced by the full-image generation based face forgery process.

Sha *et al*. [] systematically studies the detection and attribution of fake images generated by diffusion models. They compare the results of image-only input and mixed input (images and corresponding text descriptions) to explore the detection and tracing capabilities of CNN classification models. Corvi *et al*. [] analyzes the frequency domain and model identification capabilities, concluding that diffusion-generated images have unique fingerprints similar to GAN images. Wang *et al*. [] find that the diffusion reconstruction effect of fake images is superior to that of real images. They use the difference between the reconstructed image and the original image, called Diffusion Reconstruction Error (DIRE), for binary classification to determine authenticity, showing higher generalization ability. However, these methods are tested on small, self-created datasets, and their experimental conclusions lack generality. Additionally, they do not specifically focus on detecting face forgeries. Currently, the detection of faces generated by diffusion models remains relatively unexplored.

### A.2 DeepFaceGen Detailed Statistical Data

In order to construct a robust and extensive benchmark for the detection of face forgery, we carefully consider a range of critical factors including the manner of generation, generation framework, content diversity, ethnic fairness, and label richness throughout the benchmark development process. Following this, we provide detailed introduction to the forged face samples and authentic face samples in DeepFaceGen.

Forged Face Samples. The forged face samples of DeepFaceGen consists of $34$ types of forgery methods. The number of forged images/videos reaches $350,264$/$423,548$. For content diversity, we collected $143,579$ forged images and $93,497$ forged videos from [] and []. As shown in Figure 6, the forged images contain $27$ forgery methods, including localized editing based and full-image based generation. Forged samples between both generation methods are roughly balanced. The localized editing based samples include face swapping, face reenactment and face alteration. In the full-image based generation, sufficient Text2Image and Image2Image samples are generated according to the input modality. At the video-level, a rough balance is similarly maintained between the samples generated by the $16$ forgery methods. In the process of generating forged video/image samples, in order to maintain ethnic fairness, we control the balance of skin color through text prompt in full-image based generation. Localized editing based samples also fit ethnic fairness by employing SkinToneClassifier []. Additionally, we employ YOLO [] with manual screening to eliminate low-quality data. The detailed forged statistical data can be seen in Table 2.

Authentic Face samples. In order to ensure content diversity and ethnic fairness in the authentic face samples used in DeepFaceGen, we obtained real samples from reputable sources including  [],  [],  [], and  []. Specifically, we collected $482$ and $463,101$ real images from [] and [], and $19,942$, $590$, $99,630$, $193,245$ real videos from  [], [],  [], and [].
The final collection consists of $463,583$ images and $313,407$ videos, encompassing diverse ages, genders, skin tones, expressions, hair styles, hair colors, backgrounds, dressing styles, and glasses.

Figure: Figure 6: Composition and porportion illustration of image- and video-level sets. At the image-level, DeepFaceGen utilizes $27$ face forgery methods. At the video-level, it employs $16$ methods. In both levels, the forged data maintains an approximate balance between localized editing based face forgery technology and full-image generation based face forgery technology.
Refer to caption: extracted/5666211/fig4.jpg

**Table 2: Detailed Statistical Data of DeepFaceGen.**
| Manner | Subset | Methods | Images | Videos | Labels |
| --- | --- | --- | --- | --- | --- |
| Localized editing | Face Swapping | FaceShifter | 10,500 | 14,387 | n-way labels |
| FSGAN | 10,500 | 55,205 |  |  |  |
| DeepFakes | 10,500 | 6,000 |  |  |  |
| BlendFace | 10,500 | 13,491 |  |  |  |
| DSS | 10,500 | 2,866 |  |  |  |
| SBS | 10,500 | - |  |  |  |
| MMReplacement | 10,500 | 1,461 |  |  |  |
| SimSwap | - | 27,786 |  |  |  |
| Face Reenactment | Talking Head Video | 9,203 | 28,935 | n-way labels |  |
| ATVG-Net | 10,500 | 11,273 |  |  |  |
| Motion-cos | - | 22,811 |  |  |  |
| FOMM | 10,235 | 42,411 |  |  |  |
| Face Alteration | StyleGAN2 | 10,263 | - | n-way labels |  |
| MaskGAN | 8,613 | - |  |  |  |
| StarGAN2 | 10,500 | - |  |  |  |
| SC-FEGAN | 10,500 | - |  |  |  |
| DiscoFaceGAN | 10,500 | - |  |  |  |
| Full-image | Text2Image | OJ | 28,203 | - | n-way labels<br>prompt labels |
| SD1 | 25,677 | - |  |  |  |
| SD2 | 20,898 | - |  |  |  |
| SDXL | 22,839 | - |  |  |  |
| Wenxin | 9,989 | - |  |  |  |
| Midjourney | 9,784 | - |  |  |  |
| DF-GAN | 40,320 | - |  |  |  |
| DALL·E | 8,000 | - |  |  |  |
| DALL·E 3 | 2,000 | - |  |  |  |
| Text2Video | AnimateDiff | - | 40,320 | n-way labels<br>prompt labels |  |
| AnimateLCM | - | 35,642 |  |  |  |
| Hotshot | - | 40,320 |  |  |  |
| Zeroscope | - | 40,320 |  |  |  |
| MagicTime | - | 40,320 |  |  |  |
| Image2Image | Pix2Pix | 9,620 | - | n-way labels<br>prompt labels |  |
| SDXLR | 9,990 | - |  |  |  |
| VD | 9,130 | - |  |  |  |
| Total | 350,264 | 423,548 |  |  |  |

### A.3 Detailed Descriptions of Prompts Construction

In the design of prompts, we strive to achieve both content diversity and fairness, which are accompanied by a strong emphasis on detailed prompt descriptions. Following this, we designed a complete expressive framework for each prompt sentence based on the face information that humans take into account when describing faces. The prompt sentence framework contains $9$ description attributes: ages, genders, skin tones, expressions, hair styles, hair colors, backgrounds, dressing styles, and glasses. Each description attribute contains a detailed scenario situation. By iterating through the combination of $9$ attributes, we can generate over $40,000$ prompts. This design ensures data balance across the various text attributes. Then, we use LoRA [] to fine-tune the selected pretrained model and generate forged samples fine-tuned with deepfake samples. The detailed pipeline of prompts construction is shown in Figure 7.

Figure: Figure 7: Pipeline of prompts construction. It consists of four parts: the establishment of face description information, the construction of description attributes, the fine-tuning of pre-trained models and the generation of forged samples. After establishing comprehensive attributes to describe face information from images and videos, rich and comprehensive text prompts can be obtained by iterating the combination of description attributes. Then, LoRA[] is used to fine-tune the generative model to the field of face generation for the final generation task.
Refer to caption: extracted/5666211/fig2.jpg

### A.4 Evaluation Details

In this section, we provide a detailed introduction to the selected forgery detection methods and disclose the implementation details during the experimental process.

#### A.4.1 Forgery Detection Models

Following the basic backone used by the $13$ forgery detection methods, we introduce the forgery detection methods in detail.

- •
MesoNet [] is a face forgery detection algorithm based on mid-level information from image noise. This approach effectively addresses the challenges of diminished image noise and the difficulty of distinguishing forged video frames using high-level semantic features. Its shallow architecture enhances sensitivity to medium and large-scale features, thereby improving the capability of detecting facial characteristics.
- •
Xception [] is a convolutional neural network architecture entirely based on depthwise separable convolution layers, simplifies the decoupling of channel correlation and spatial correlation to derive depthwise separable convolutions. This enables efficient extraction of complex features from images and video frames.
- •
EfficientNet-B0 [] is the baseline network of the
EfficientNet family, which is developed by leveraging a multi-objective neural architecture search based
on mobile inverted bottleneck MBConv [] with
squeeze-and-excitation optimization [] added to it.
- •
F3-Net [] utilizes two complementary frequency-aware cues: frequency-aware decomposed image components and local frequency statistics. These cues are deeply explored through a dual-stream collaborative learning framework to detect subtle forgery patterns.
- •
RECCE [] is a reconstruction and classification learning framework designed to learn common characteristics of real faces by reconstructing face images. It trains a reconstruction network using real face images and employs the latent features of this network to classify real and forged faces. Due to the inconsistency in data distribution between real and forged faces, the reconstruction errors for forged faces and can accurately highlight the forged regions.
- •
DNANet [] adopts
pre-training on image transformation classification and
patchwise contrastive learning to capture globally consistent features that are invariant to semantics. It can focus on architecture-related traces and strengthen the global consistency of extracted features.
- •
FreqNet [] is a lightweight frequency space learning network designed for generalizable forgery image detection. This approach leverages the power of frequency domain learning, providing an adaptable solution for the challenging problem of deepfake detection across diverse sources and GAN models. The methodology includes practical and compact frequency learning plugin modules that integrate with CNN classifiers to enable them to operate effectively within the frequency domain.
- •
CViT [] is a model composed of two main components: Feature Learning (FL) and the Vision Transformer (ViT). The FL component, a stack of convolutional operations without a fully connected layer, extracts features from face images. These features are then processed by the ViT, which converts them into a sequence of image pixels for detection.
- •
SLADD [] aims to generalize well in unseen scenarios. It operates on the principle that a generalizable detector should be sensitive to various types of forgeries. SLADD enriches the diversity of forgeries by synthesizing augmented forgeries using a pool of forgery configurations and enhances sensitivity by training the model to predict these configurations.
- •
Exposing [] is an information bottleneck-based framework for deepfake detection that aims to extract broader forgery clues. It captures a wide range of forgery clues by extracting multiple non-overlapping local representations and fusing them into a global, semantically rich feature.

#### A.4.2 Implementation Details

Preproccess. The image and video datasets are divided into training, validation, and test subsets in a ratio approximately $7:1:2$. To ensure fairness in evaluation, each subset maintains a ratio of real to fake instances close to $1:1$. For video-level
evaluations, the video files in the dataset need to be extracted and stored as individual video frames. Given the varying lengths of the video files we collected and generated, we standardize the number of frames extracted from each video to $24$. Additionally, since the authors of SLADD [] did not disclose the process for creating masks, we adopted the following approach: the mask for real data is set to an all-zero matrix, indicating that there are no forgery regions in the input image. For forged data, we use YOLO [] to obtain the face bounding box, and then convert the bounding box into a binary mask image, with the forgery region set to $1$ and all other areas set to 0 0.

Training. We all follow the original hyperparameter settings in the evaluation methods. The loss function for SLADD [] is set to MSE, while the loss functions for MesoNet [], EfficientNet-B0 [], Xception [], F3-Net [], DNANet [], RECCE [], and CViT [] are set to CrossEntropyLoss. In particular, based on CrossEntropyLoss, Exposing [] designed the local information loss based on the theoretical analysis of mutual information to ensure the orthogonality and adequacy between local features. The optimizer for all models is Adam with a learning rate of $1\times 10^{-5}$. The batch size is set to $128$. All models are pre-trained on ImageNet. All images in the dataset were resized to a fixed resolution of $299\times 299$ pixels and normalized to have pixel values in the range [0 0, $1$].

Inference. We only perform single-crop inference, and directly scale the input face image to the input spatial size of the model.

Figure: Table 3: The AUC scores of Cross-generalization Ability Verification Experiments (D: Detection technique, F: Forgery method).

Figure: Figure 8: The cross-generalization ability comparison for various image-level forgery detection methods. The horizontal axes represent $5$ categories of image forgery techniques. All forgery detection methods are trained on the FaceShifter subset, which has demonstrated the best generalization performance among the detection techniques described in the main manuscript. These methods are subsequently tested using samples generated by the aforementioned forgery techniques.
Refer to caption: extracted/5666211/zhuxing_image.jpg

Figure: Figure 9: The cross-generalization ability comparison for various video-level forgery detection methods. The horizontal axes represent $3$ categories of localized editing-based video forgery techniques. All forgery detection methods are trained on the DSS subset, chosen for its superior generalization performance among the detection techniques described in the main manuscript. Subsequently, these methods are tested using samples generated by the aforementioned video-level forgery techniques.
Refer to caption: extracted/5666211/zhuxing_video.jpg

### A.5 Details for Cross-generalization Ability Verification Experiments

In this section, we employ $13$ forgery detection methods to evaluate the cross-generalization capabilities among sub-datasets. The forgery detection methods are first trained on the subsets that exhibited the best generalization performance in the broad capability evaluation experiments of different forgery techniques discussed in the main text ( FaceShifter subset at the image level and DSS subset at the video level). Subsequently, the generalization performance is tested across various subsets. As shown in Figure 8 and Figure 9, models with detail extraction modules, such as Exposing [], FreqNet [] and RECCE [], achieve higher evaluation metrics for identifying editing forged data, which corresponds to *Finding 1*. During the generalization test from localized editing forgery to full-image generation forgery, it is easier to detect data generated by DF-GAN, further validating *Finding 3*. Additionally, when using localized editing forgery images/videos as training data, the internal generalization ability of video forgery detection models is significantly lower than that of image forgery detection models, further confirming *Finding 9*. The detailed experimental results can be viewed in Table 3.

Figure: Figure 10: The forgery feature visualization for different forgery techniques on image-level (a-c) and video-level (d) datasets with t-SNE []. (a) different generation frameworks, (b) different input modalities, (c) and (d) different generation manners.
Refer to caption: extracted/5666211/tsne_buchong.jpg

### A.6 Fine-grained Analysis of Forgery Detection Feature

As shown in Figure 10, we conduct a fine-grained visual analysis of forgery detection features. Based on Figure 10 (a), it is evident that the forgery features of GAN-based model are significantly different from those of Diffusion-based and Autoregressive-based models. This phenomenon provides an explanation for *Finding 3* from the perspective of feature distribution. In Figure 10 (b), the forgery feature distributions are similar when using text and image as input modalities, which corresponds to *Finding 4*. Additionally, Figures 10 (c) and (d) demonstrate that *the forgery features of localized editing techniques do not show significant differences between images and videos (Finding 11)*.

### A.7 Challenges and Future Work

In light of the rapid advancements in face generation techniques, the progress of face forgery detection techniques has significantly lagged behind. Extensive experimentation and analysis reveal several deficiencies in the current forgery detection methods, including inadequate identification accuracy, limited generalization capabilities, and restricted scope for detecting various types of forgery. This section provides a comprehensive overview of the existing challenges in face forgery detection and offers potential valuable directions for future research.

#### A.7.1 Challenges

- •
Difficulty in Handling Complex Scenarios. The diversity of complex scenarios increases the difficulty of face forgery detection tasks. Real-world face forgery detection can be affected by environmental factors such as changes in lighting conditions, which can alter shadows and highlights on the face, making it appear darker or brighter. Changes in camera angles can distort facial shapes and features, making the face look twisted or misaligned. Additionally, variations in background complexity can blur the edges of the face or blend it with the background, making it appear unclear or disproportionate. These factors can impact the authenticity and reliability of detection results, increasing the difficulty of recognizing and detecting forgeries.
- •
Poor Generalization Performance. Although current detection models perform well on individual face forgery datasets, their generalization across different datasets remains inadequate. In real-world scenarios, the type of face forgery method used is often unknown, making it difficult to determine the specific type of forgery. Therefore, using pre-trained face forgery detection models for real-world tasks may result in unreliable detection outcomes.
- •
Oversimplified Forgery Detection Tasks. Current face forgery detection tasks focus primarily on binary classification of whether the content is forged, which is relatively crude. In real-world scenarios, there is often a need for tracing the source of the forgery, which is crucial for determining responsibility and uncovering the truth. In face video forgery tasks, attackers often target only a few video frames or audio segments to alter the video content. However, forgery detection models that focus on video-level forgery detection can easily overlook the characteristics of forged segments, significantly increasing the likelihood of detection errors.

#### A.7.2 Future Work

- •
Objective Quantification of Evaluation Benchmarks. With the increasingly complex and realistic content forgery scenarios brought about by the development of AIGC technologies, current evaluation benchmarks rely on specific model performance metrics, which can be limiting. In real-world scenarios, designing evaluation benchmarks that can accurately quantify the multi-angle forgery detection capabilities and even the adaptability of models is a crucial direction for future exploration.
- •
Dynamic Updating of Benchmark Data. When designing evaluation benchmarks, it is essential to consider the existence of diverse face forgery types. Regularly updating benchmark datasets to include the latest forgery techniques can help the benchmarks stay close to the complex real-world scenarios. Integrating user feedback data can provide new ideas for dynamically updating benchmark datasets. Additionally, as deep forgery technologies continue to evolve, establishing a dynamic labeling mechanism to address new deep forgery techniques and generative models is becoming increasingly important.
- •
Building General Forgery Detection Scenarios. Although we have constructed a general face deep forgery detection dataset that includes both localized editing based and full-image generation based face forgery techniques, incorporating both image and video modalities, the audio aspect remains a gap. Furthermore, given the relatively unexplored state of detecting face forgeries generated by diffusion methods, designing general forgery detection techniques based on the inherent differences between real and forged videos, as well as the local feature similarities and model inference paths, is a critical issue that needs to be addressed in the coming years.
- •
Emphasis on Robustness of Forgery Detection Models. The robustness of forgery detection models is key to maintaining stability and reliability in real-world scenarios with complex and variable content. Introducing adversarial samples during training and testing can enhance the robustness of models. However, while adding noise and adversarial samples can improve robustness to some extent, it can also lead to a loss in detection performance. Exploring the inherent characteristics of real samples to identify differences between forged and real samples and developing detection methods that can handle any face forgery product while ensuring detection accuracy is a primary research direction for the future.
- •
Self-Evolving Forgery Detection Frameworks. Forgery techniques and forgery detection techniques are mutually aligned and promote each other. Forgery technologies generally advance faster than forgery detection technologies, leading to significant harm from forged face products to human society. Current forgery detection models and methods rely mainly on researchers analyzing the flaws and weaknesses of forgery technologies and designing corresponding solutions. Developing self-evolving frameworks using adversarial learning mechanisms and reinforcement learning models to drive the autonomous evolution of forgery detection models, thereby improving the ability to quickly respond to various forgery products, is a key research direction for the future.

Figure: Figure 11: Comparative evaluation of various forgery detection techniques on image-level samples from different attribute perspectives, including (a) age attribute, (b) skin tone attribute, (c) hair style attribute,(d) hair color attribute, (e) expression attribute, (f) background attribute, (g) gender attribute, (h) glasses attribute, and (i) dressing style attribute.
Refer to caption: extracted/5666211/shuxing_image.jpg

Figure: Figure 12: Comparative evaluation of various forgery detection techniques on video-level samples from different attribute perspectives, including (a) age attribute, (b) skin tone attribute, (c) hair style attribute,(d) hair color attribute, (e) expression attribute, (f) background attribute, (g) gender attribute, (h) glasses attribute, and (i) dressing style attribute.
Refer to caption: extracted/5666211/shuxing_video.jpg

### A.8 Fine-grained Attribute Statistic Analysis for Different Forgery Techniques

In this section, we train all forgery detection models using the training samples obtained from DeepFaceGen. Subsequently, we utilize the fine-grained labels provided by DeepFaceGen to conduct a detailed analysis of the detection patterns of the forgery detection techniques across $9$ attributes.

Age Attribute. The age attribute significantly impacts the effectiveness of forgery detection models. Figures 11 (a) and 12 (a) indicate that forgery detection models face more challenges with detecting forgery samples of children, while it is easier to detect forgery data of elderly faces. This difference is due to the unique facial characteristics of children and the elderly. Children’s facial features are finer and smoother, lacking prominent wrinkles and details, which makes it easier for forgery techniques to generate realistic child faces, thereby increasing the difficulty of detection. In contrast, elderly individuals often have more pronounced and complex facial features, including wrinkles, age spots, and sagging skin, which make forgery more challenging and, therefore, more likely to be detected by the model.

Skin Tone Attribute. The effectiveness of forgery detection models varies with different skin tones. Figures 11 (b) and 12 (b) show that these models have greater difficulty in accurately detecting forgeries in individuals with darker skin tones compared to those with lighter skin tones. This highlights a racial bias inherent in the forgery detection techniques. The potential cause of this bias could be linked to variations in skin tones and the influence of lighting conditions. Individuals with darker skin tones may have facial features that are harder to capture in forgery detection. Darker skin tones can result in lower contrast in facial details, such as shadows and highlights, making it difficult for forgery detection models to identify forgery artifacts. Conversely, the facial features of individuals with lighter skin tones are generally easier to capture in images. Lighter skin tones make facial details, such as wrinkles and subtle expressions, more visible and typically maintain better facial detail contrast under various lighting conditions.

Hair Style Attribute. The variety of people’s hairstyles also has an impact on the effectiveness of forgery detection. As shown in Figures 11 (c) and 12 (c), detecting forgeries with the curly hair attribute is more difficult, while detecting those with the bald attribute is easier. In video-level experiments, the detection performance is relatively consistent across different attributes. We infer that curly hair, with its highly complex and irregular structure, contains rich details between strands. This complexity poses a greater challenge for forgery techniques in generating curly hair, making it easier to leave behind subtle artifacts that are difficult to detect. Consequently, detection models struggle to differentiate these subtle differences, increasing the difficulty of detecting forgeries with curly hair. In contrast, forgery techniques tend to produce more consistent results when generating bald heads due to the lack of complex hair structures, making it easier for detection models to identify forgery artifacts. Additionally, in video-level experiments, the continuity and motion information assist the forgery detection models in capturing forgery artifacts more effectively, leading to more balanced detection performance across different hair style attributes.

Hair Color Attribute. Figure 11 (d) and Figure 12 (d) show that forgery detection models perform relatively evenly when detecting forged data with the attributes of brown hair, blonde hair, and black hair. This can be attributed to similar details and contrast under lighting conditions. When generating forged images, forgery techniques typically handle similar textures and lighting effects for all three hair colors. This similarity results in detection models not having significant difficulty differences in identifying these forgeries.

Expression Attribute. People’s inner emotions can be externalized into different expressions. Based on (e) in Figure 11 and Figure 12, it is apparent that forgery detection models perform well when detecting forged images with the anger and surprise attributes. This may result from the facial expressions of anger and surprise attributes. They contain rich details and features that are easier to extract and recognize in image processing. Tense facial muscles and deep wrinkles are typical features of anger, while an open mouth and raised eyebrows are clear indicators of surprise. Forgery detection models can use these prominent features to enhance detection accuracy.

Background Attribute. The background in images/videos also influences the performance of forgery detection models. Figures 11 (f) and 12 (f) indicate that forgery detection models find it easier to detect forged images with the countryside attribute and harder to detect those with the home attribute. Background complexity may be a direct factor. Countryside backgrounds generally have lower complexity, featuring large natural landscapes such as fields, trees, and skies. These elements are relatively simple and have fewer variations, making it easier for forgery techniques to generate these backgrounds without introducing complex artifacts. Consequently, detection models can more easily identify forged elements in these simple backgrounds. By contrast, home backgrounds typically include many details and complex objects such as furniture, appliances, and decorations. Detection models need to process more details and variations, making it harder to detect forgeries.

Gender Attribute. The accuracy of forgery detection models is often lower for female samples ((g) in Figure 11 and 12). Similar to children in age attribute, female facial features are generally finer and smoother, lacking prominent wrinkles and rough skin texture. These fine features may make it harder for detection models to capture forgery artifacts. Additionally, women tend to wear makeup in greater numbers than men. Cosmetics can enhance or conceal certain facial features, and introduce artificial details such as eyeliner and lipstick. These changes can also make it more challenging for forgery detection models to distinguish between real and forged images, as the makeup may mask subtle forgery artifacts that the model relies on for detection.

Glasses Attribute. Based on Figure 11 (h) and Figure 12 (h), forgery detection models perform similarly when detecting forged data with and without the glasses attribute. This can be attributed to glasses’ simple and fixed geometric features (such as frames and lenses). When generating faces with glasses, forgery techniques can maintain the stability of these geometric features well, resulting in forged images of similar quality to those without glasses.

Dressing Style Attribute. It can be found from Figure 11 (i) and Figure 12 (i) that forgery detection models perform similarly when detecting forged data with the casual wear attribute and the formal wear attribute. This may due to their similar complexity. Although casual and formal wear differ in style, the complexity of details in both types of clothing is relatively similar. Formal wear may include more details (such as ties and buttons), but these details do not significantly affect the quality of forged images. Casual wear may have more varied styles, but its complexity is comparable to formal wear.

### A.9 Potential Negative Social Impacts

The creation and use of deepfake datasets, while beneficial for advancing technology, can lead to several negative societal impacts:

- •
Misuse of Forgery Methods. In order to restore the complex forgery scenes in the real scene as much as possible, the forgery methods in the data set are realistic. These forgery methods can be misused to create misleading or harmful content, eroding public trust in media and making it difficult to distinguish between real and fake information.
- •
Ethical Concerns. Due to the transparency of the data set, a large number of face samples in the data set may provide fake resources for illegal personnel. Widespread exposure to deepfakes can lead to public skepticism and paranoia about the authenticity of all digital content.

To mitigate these impacts, we are contemplating controlled access for users and are committed to the dynamic evolution of DeepFaceGen to ensure it remains robust against emerging threats.

## Peer Reviews

confidence: 5
contribution: 3
initial_rating: 5
presentation: 3
questions: I hope the authors can provide more constructive insights for designing deepfake detection methods according to the proposed benchmark, such as:

*   For model-centric methods, what impact do various architectures (e.g., CNN vs. Transformers) or modalities (e.g., frequency vs. noise) have on different kinds of deepfakes?
*   For data-centric methods, which data augmentations or training strategies are beneficial for deepfake detection?
soundness: 3
strengths: This paper is easy-to-follow. This work discusses large-scale deepfakes generated by a variety of methods, including recent prompt-guided deepfakes, which is a limitation of prior datasets.
Since deepfake media are rapidly evolving, a reliable and cutting-edge benchmark has foreseeable value in the field of deepfake forensics.
summary: This work proposes a large-scale benchmark for the deepfake detection task, and the authors draw some findings through extensive experiments.
weaknesses: While the authors present up to 13 findings, these findings are not impressive enough. There are some shortcomings in the claimed findings including:
*   Some findings seem to simply reassert well-known motivations from previous works, such as Finding 1 with MAT(Zhao et al, CVPR 2021).
*   Some findings are common sense, such as Finding 2&7.

Furthermore, it is recommended that authors consolidate a Findings list in the conclusion part, as the presented Findings are dispersed throughout the 36 pages of the whole paper, making it less friendly for reading.

---

confidence: 3
contribution: 3
initial_rating: 6
presentation: 3
questions: See weaknesses.
soundness: 3
strengths: 1. The paper constructs a large face forgery dataset, utilizing a variety of generation methods, including Prompt-guided Generation techniques such as Text2Image, Image2Image, and Text2Video, as well as Task-oriented Generation techniques. Compared to previous datasets, this dataset employs a more comprehensive and diverse range of generation methods, resulting in a significantly larger scale.

2. The paper employs 20 detection methods and conducts extensive experiments and analyses using DeepFaceGen.
summary: The paper introduces DeepFaceGen, a benchmark specifically developed for face forgery detection. DeepFaceGen includes 463,583 real images, 313,407 real videos, 350,264 forged images, and 423,548 forged videos, generated using 34 common image/video generation techniques. The authors comprehensively evaluate 20 mainstream face forgery detection techniques using DeepFaceGen.
weaknesses: 1. The paper may not present a methodological contribution in itself, but the large and comprehensive dataset is valuable.
2. I have some questions regarding missing details. The authors selected 20 detection methods, including both image-level and video-level approaches, but did not clarify the representativeness of these methods.  Additionally, while the authors conducted analyses of texture and frequency domain features, the chosen methods seem to focus more on detection models that utilize frequency domain features.

---

confidence: 5
contribution: 2
initial_rating: 5
presentation: 3
questions: 1. What is the meaning of "prompt-guided" and "task-oriented"? What is the rationale or reference for this definition of forgery types?

2. Which findings presented in this work are truly new to the field and have not been shown in existing or previous works?

3. Several recent face forgery datasets such as those mentioned in ref[1,2,3,4,5] are missing from the discussion. What are the advantages and additional contributions of the current dataset over these previous ones?

4. What are the deeper insights regarding the differences between image and video face forgery detectors?

5. Most of the findings and evaluations are empirical observations. What are the underlying reasons behind these observations?

ref[1]: Diffusion Facial Forgery Detection, ArXiv 2024. 

ref[2]: DiffusionFace: Towards a Comprehensive Dataset for Diffusion-Based Face Forgery Analysis, ArXiv 2024. 

ref[3]: Contrastive pseudo learning for open-world deepfake attribution, ICCV 2023. 

ref[4]: DF40: Toward Next-Generation Deepfake Detection, NeurIPS 2024. 

ref[5]: AI-Face: A Million-Scale Demographically Annotated AI-Generated Face Dataset and Fairness Benchmark, ArXiv 2024.
soundness: 2
strengths: - This work implements 34 distinct face forgery techniques for creating fake data, although this work does not create new real faces in their dataset.
- This work involves both spatial and temporal detection methods for comparison, which is new in the existing face forgery benchmark.
- Some findings are new to me such as Finding-10.
summary: This work presents a face deepfake dataset that incorporates 34 face forgery generation techniques. The author conducts training and evaluation experiments on this dataset and presents several findings. This author implements both image and video detectors for a more comprehensive comparison.
weaknesses: **1. Unclear and ambiguous definitions for the forgery types:** In this paper, face forgery techniques are categorized into two main types: prompt-guided and task-oriented. However, both of these concepts can be rather abstract and unclear, potentially misleading the community. Specifically:
- In Line 48, the author mentions for the first time that "current deepfake datasets focus on relatively outdated task-oriented based face forgery techniques." However, the meaning of "task-oriented-based face forgery" is not clear. To the best of my knowledge, I have not found any existing research or survey that provides such a definition of face forgery techniques. The author must either provide **appropriate citations and references or clearly define the rationale behind this definition**.
- If the author cannot provide accurate references or detailed explanations, the categorization will remain very unclear and ambiguous. I am not sure whether this category is reasonable and can be accepted by the community.


**2. Several findings in this work have been verified by previous works and might be trivial:**
- Finding-1 and Finding-5 are quite similar, indicating the need to extract more detailed information. In fact, many SOTA detectors have proposed different approaches to enhance detection performance by leveraging more discriminative artifacts or detailed information. So, the conclusion that these SOTA detectors can achieve better performance than the vanilla backbone such as Xception can be rather trivial.
- Finding-11 concludes and highlights the benefit of using different levels of frequency clues for detection. However, **many previous (very early) works have already provided this conclusion**, such as F3Net (ECCV'20), SRM (CVPR'21), and SPSL (CVPR'21). These works reached these conclusions using very old benchmark datasets such as FF++ (ICCV'19). But the author seems to use the latest benchmark and still concludes the same conclusion. What is the significance of creating such a benchmark?
- Finding-6: "existing diffusion-based generation techniques possess a comparable ability to generate forged videos." The same conclusion has been proposed in works like ref[1], making this conclusion trivial.


**3. About the correctness of several findings:**
- In Finding 6, the authors use "detection performance" to infer "the generation quality of diffusion techniques." However, the two may **have a correlation but not a causal relationship**. The worst detection performance can be attributed to many scenarios, while the metrics for quality are FID-like tools used in the image synthesis field. Misconnecting these two concepts is inappropriate. Specifically, I note that diffusion-generated images can have different poses and facial sizes compared to other data, which makes their distribution differ from other deepfake data (as seen in the left-up and right-below corners in Figure 7). But this does not mean that diffusion-generated images are not realistic.


**4. Reproduction Concerns:** 
- I have checked the link (`https://anonymous.4open.science/r/DeepFaceGen-47D1`) provided by the author. It seems to only contain the `model.py` files. Without detailed preprocessing, training, and evaluation codes, it is hard to see whether the results are reproducible and the experimental settings are fair.

ref[1]: Diffusion Models Beat GANs on Image Synthesis, NeurIPS'21.

---

confidence: 4
contribution: 3
initial_rating: 6
presentation: 2
questions: See the Weaknesses section
soundness: 3
strengths: 1. The dataset construction demonstrates substantial effort, integrating both task-oriented and prompt-guided forgery samples, while considering racial fairness and content diversity.

2. The experimental analysis reveals several noteworthy findings through comparative studies of detection methods, particularly in understanding the differences between generation frameworks and their generalization capabilities.

3. The paper provides comprehensive technical content, including attribute-level analysis and clear experimental protocols, making it a useful reference for future research in face forgery detection.
summary: This paper presents DeepFaceGen, a large-scale evaluation benchmark for face forgery detection combining both task-oriented and prompt-guided generation approaches. The dataset comprises 776,990 real and 773,812 forged face samples (images and videos) generated using 34 different forgery techniques. The authors evaluate 20 mainstream forgery detection methods on this benchmark and derive several key findings, including: (1) detail extraction modules play a crucial role in forgery detection performance; (2) autoregressive-based and diffusion-based techniques produce more realistic forged faces than GAN-based approaches; (3) task-oriented forgeries show better generalization capability than prompt-guided ones.
weaknesses: 1. The presentation of experimental results lacks clarity, particularly in crucial Figures 1, 2, and 3, which appear cluttered and fail to effectively highlight key findings. The use of small relative bar charts without absolute performance numbers significantly limits the paper's utility as a benchmark study.

2. The claimed findings lack rigorous support and appear overly definitive. Many conclusions are drawn from basic performance comparisons without considering the multiple factors that could influence detection performance. The authors should be more cautious in their assertions and provide stronger empirical evidence for each finding.

3. The crucial issue of generalization capability is inadequately addressed. Figure 3's visualization of cross-generalization results is difficult to interpret, and the corresponding tables in Appendix F are poorly organized. Additionally, the paper lacks comparison with methods specifically designed for generalization, instead focusing mainly on in-domain detection approaches.

4. Section 4.3 and Figure 4's feature visualization analysis is unclear, with the figure being particularly difficult to distinguish. It's not evident how this analysis contributes to the paper's overall findings.

Overall, while the paper attempts to cover extensive ground, it suffers from imprecise presentation and insufficiently supported conclusions. A substantial revision is needed to improve the result presentation, strengthen the empirical support for findings, and make the content more accessible to readers.

Meta-review: The paper introduces DeepFaceGen, a comprehensive and large-scale evaluation benchmark designed to quantitatively assess the effectiveness and generalizability of face forgery detection techniques. The benchmark dataset consists of real and fake images/videos collected from various sources, generated using both task-oriented and prompt-guided methods. The reviewers acknowledge that this benchmark is novel compared to existing alternatives. However, their main concern lies with the novelty of the findings and the experimental evidences provided to support them.

Most of the findings presented in the paper, based on experiments using the new dataset, have already been reported in the literature. This raises the question of whether introducing a new dataset is necessary if it fails to reveal insights that are not already known. Although the authors addressed this concern in their response, reviewer 3GRu did not acknowledge their reply, and I also found it unsatisfactory.

Additionally, the authors referred to specific sections of an anonymous website to address some reviewers' questions and comments. Unfortunately, these sections were unavailable. As none of the reviewers responded to the authors’ rebuttal, I was unable to determine whether the authors provided satisfactory answers due to the missing sections on the anonymous website.

Decision: Reject