GenImage: A Million-Scale Benchmark for Detecting AI-Generated Image
Authors: Mingjian Zhu
Hanting Chen
Qiangyu YAN
Xudong Huang
Guanyu Lin
Wei Li
Zhijun Tu
Hailin Hu
Jie Hu
Yunhe Wang
Venue: NeurIPS 2023 Datasets and Benchmarks Poster (2023)
Keywords: AI Generated Image Detector; Generative Model

Abstract: The extraordinary ability of generative models to generate photographic images has intensified concerns about the spread of disinformation, thereby leading to the demand for detectors capable of distinguishing between AI-generated fake images and real images. However, the lack of large datasets containing images from the most advanced image generators poses an obstacle to the development of such detectors. In this paper, we introduce the GenImage dataset, which has the following advantages: 1) Plenty of Images, including over one million pairs of AI-generated fake images and collected real images. 2) Rich Image Content, encompassing a broad range of image classes. 3) State-of-the-art Generators, synthesizing images with advanced diffusion models and GANs. The aforementioned advantages allow the detectors trained on GenImage to undergo a thorough evaluation and demonstrate strong applicability to diverse images. We conduct a comprehensive analysis of the dataset and propose two tasks for evaluating the detection method in resembling real-world scenarios. The cross-generator image classification task measures the performance of a detector trained on one generator when tested on the others. The degraded image classification task assesses the capability of the detectors in handling degraded images such as low-resolution, blurred, and compressed images. With the GenImage dataset, researchers can effectively expedite the development and evaluation of superior AI-generated image detectors in comparison to prevailing methodologies.

GenImage: A Million-Scale Benchmark for Detecting AI-Generated Image
Mingjian Zhu, Hanting Chen, Qiangyu Yan, Xudong Huang, Guanyu Lin, Wei Li, Zhijun Tu, Hailin Hu, Jie Hu, Yunhe Wang"
Huawei Noah's s Ark Lab {zhumingjian, yunhe. wang)
Abstract
The extraordinary ability of generative models to generate photographic images has intensified concerns about the spread of disinformation, thereby leading the demand for detectors capable of distinguishing between Al-generated fake images and real images. However, the lack of large datasets containing images from the most advanced image generators poses an obstacle to the development of such detectors. In this paper, we introduce the GenImage dataset, which has the following advantages: 1) Plenty of Images, including over one million pairs of Al-generated fake images and collected real images. Rich Image Content, encompassing broad range of image classes. 3) State-of-the-arr Generators, synthesizing images with advanced diffusion models and GANs. The aforemen- tioned ad advantages allow the detectors trained on GenImage to undergo a thorough evaluation and demonstrate strong applicability to diverse images. We conduct a comprehensive analysis of the dataset and propose two tasks for evaluating the detection method resembling real-world scenarios. The cross- gessereeerrtor image classification task measures the performance of detector trained on one generator when tested on the others. The degraded image classification task assesses the capa- bility of the detectors in handling degraded images such as low-resolution, blurred, and compressed images. With the GenImage dataset, researchers can effectively expedite the development and evaluation of superior Al Al-generated image detectors in comparison to prevailing methodologics.
1 Introduction
The advancement of generative models has yielded remarkable progress in synthesizing photorealistic images, drastically reducing the requisite expertise and effort to generate fake images. This unprece- dented accessibility has evoked concerns regarding the ubiquitous dissemination of disinformation. Fake images are particularly convincing because of visual comprehensibility. Therefore they enable a negative impact on social areas such as politics and economics by manipulating public opinion. fire are widely shared Twitter [22]. This
For example, Al-generated photos of the Pentagon image fools several major news outlets and causes a significant drop in the US stock market. Current ofteeof-thheart art (SOTA) generative models have exhibited the ability to generate images that pose significant challenge to human perception and discrimination. Humans can only achieve an accuracy rate of 61.3% when discriminating between real images and Al-generated fake images [19]. To this end, it is of utmost urgency to prioritize the development of an effective detector capable accurately identifying fake images generated by these advanced generative models.
To aid in the development of detectors, early fake image detection dataset (30 3 primarily concentrate
on face forgery, utilizing generative models to manipulate the human face, thereby replacing person's s
Corresponding Author
37th Conference on Neural Information Processing Systems (NeurIPS 2023) Track on Datasets and Benchmarks.

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Table 1: An overview of fake image detection datasets.
Image
Generator Category
Public Avalibility
Real
Fake
Dataset
Content
GAN
Diftusion
Images
Images
UADFV 36
Face
K
241
252
FakeSpotter
30
Face
6,000
5,000
DFFD 3
Face
V
58,703
240,336
APFDD 7
Face
X
X
5,000
5,000
Forgery/Net
10
Face
V
1,438,201
1,457,861
DeepArt
12
Art
V
64,479
73,411
CNNSpot
General
V
362,000
362,000
IEEE VIP Cup 29
General
X 7,000
7,000
DE-FAKE 27
General
X 20,000
60,000
CiFAKE 0
General
V
60,000
60,000
GenImage
General
1,331,167
1,350,000
presents small-scale face forgery dataset, comprising solely 241 real images and 252 fake images. larger dataset, namely ForgeryNet [TO), introduces more comprehensive dataset encompassing over one million instances of face forgery images, Nevertheless, the applicability of these datasets is limited due to their sole focus on face images, rendering them less effective for a broader range of image categories. Early datasets for general Al-generated images are built upon generative adversarial network (GAN), such as CNNSpot [3]] CNNSpot only employs ProGAN [2] for generating training set and evaluates detector performance across variety of GAN-based testing set. These generators generate images bearing strong resemblance to real images, although humans could often detect their synthetic nature. Besides GAN, the recent advent of alternative generators, such as diffusion models, significantly enhances the quality of generated images, rendering the task of distinguishing real from fake images increasingly challenging. Therefore, it is essential to explore the images generated by diffusion models. IEEE VIP Cup (29] and DE-FAKE 27 have employed diffusion models to generate more general images. Based small-scale Cifarl0 dataset [15. CiFAKE [|] generates fake images only with Stable Diffusion V1.4. However, existing diffusion model datasets suffer from limited data.
identity modifying the facial attributes. UADEV 36
In this paper, we generate an extensive collection of general images leveraging the current state- of-the-art Diffusion and GAN models, subsequently constructing our dataset, namely GenImage. Our target is to be on par with awesome million-scale datasets like ImageNet 4 for complete and comprehensive Al-generated image detector training and validation. We use the 1000 class labels in ImageNet to generate 1.3 million fake images, which equals the number of real images in ImageNet. In T we compare Genlmage with the other datasets. Compared with the face forgery datasets, Genlmage covers a broader range of image content, such as basketball and guitar. Besides, Genlmage employs state-of-the-ar diffusion generators, such as Midjourney 20 and Stable Diffusion (25). The richer image content and generator categories ensure the diversity of images in Genlmage. Compared with general fake image detection datasets, GenImage also demonstrates the advantages of the dataset scale. We perform comprehensive analysis of GenImage using existing SOTA detection methods and then propose two tasks resembling real-world detection problems: (1) Cross-Generatoe Image Classification: training the detectors on the images generated by generator and evaluating the detectors on the images generated by the other generators. (2) Degraded Image Classification: evaluating the detectors the degraded images, such as low resolution, JPEG compression, and Gaussian Blur. The GenImage is promising to significantly contribute to the advancement of detectors specifically designed for detecting Al-generated images.
2 Related Work
2.1
Al-generated Image Detection Datasets
The rapid growth of generative neural networks has facilitated the generation of images, raising concerns over the propagation of misinformation due to the low-cost production of such images. Generative networks primarily employ GANs for image generation, including CycleGAN [3)]. StyleGAN [13), ProGAN [2]. These generators produce images bearing strong resemblance to real images, although humans could often detect their synthetic nature. However, the recent advent of alternative generators, such as diffusion models, significantly enhances the quality of generated images, rendering the task of distinguishing real from fake images increasingly challenging. To aid
in the development of detectors, initial synthetic image detection datasets [44]]]]]]]] [6] predominantly

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ImageNet
Midjoumey
SD V1.4
SD V1.5
ADM
GLIDE
W Wukong
VQDM
BigGAN
Samoyed
Brambling
Com
Candle
Lighthouse
Figure 1: Visualization of images on Genlmage dataset. SD is short for Stable Diffusion.
comprised large number of facial images. The detectors based on these datasets cannot effectively identify the general images such as houses. Despite the limitations of early GANs in producing general images, pioneering work has made strides, such as CNNSpot [31] which solely employs ProGAN for training dataset generation and subsequently evaluates detector performance across variety of GAN-based testing sets. A recent trend in this space involves the introduction of diffusion models as generators. For instance, DrepArt (32 leverages advanced generators such as Stable Diffusion, DALL-E2, Imagen, Midjourney. and Parti to produce art-like images. Similarly, initiatives like the IEEE VIP Cup [29] and DE-FAKE 27 have employed diffusion models to generate more general images. Based small-scale Cifarl0 dataset [15), CiFAKE [1] generates fake images with Stable Diffusion V1.4. Nonetheless, these datasets often focus on GAN, and they are data-limited.
2.2 Al-generated Image Detection Methods
Numerous approaches have emerged in the pursuit of spotting generated images based on fake image detection datasets. Among the conventional methods, image classifiers have served the most direct detectors, leveraging architectures like CNN-based ResNet50 (9, Transformer- based DeiT-S 28),
and Swin-T 17 for binary classification of images. These detectors offer solid baseline techniques. In the early stages, emphasis was placed on detectors that relied on facial features. F3Net [23]] proposes to simultaneously explore frequency components partition and discrepancy of frequency statisties between real and fake images for face forgery detection. GramNet [18] uses global texture features to make fake face detection more robust and generalizable. These methods can, to some extent, useful for the design of general image detectors. Past general image detectors have been developed and evaluated spectrum as input and synthesizes GAN artifacts in real images without the necessity of specific between the
images generated by GAN generators. Spec (38 takes the frequency
GAN models to generate fake images training data. DIRE 33 computes input and its reconstruction counterpart by a diffusion model. CNNSpot (31] uses ResNet-50 [9] as a binary classifier, with specifie pre- and post-processing and data augmentation. Most of the detectors are for GAN. The development of dedicated detectors tailored to the unique characteristies of mixed GAN and diffusion data is imperative.
model.CNNSpo
Dataset Construction
3.1 Dataset Details
To facilitate precise evaluation of detector's capacity to discern between Al-generated and real images, we construct dataset, Genlmage, comprising over one million pairs of real and fake images. Considering that ImageNet is awesome dataset that want to be par with, GenImage employs

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all the real images in ImageNet. Image generation in Genlmage leverages 1000 distinct labels in ImageNet, ensuring a near-equal distribution of real and generated images across each class. Our dataset, Genlmage, comprises 2,681,16) images, segregated into 1,331,167 real and 1, 1,350,000 fake images. The real images are subdivided into 1,281.167 images for training and 50,000 for testing. With ImageNet [4] providing 1000 distinct image classes, we generate 1350 images for each class, out of which 1300 are allocated for training and the remaining 50 for testing.
To address the problem of detecting images generated by SOTA generators, we employ eight genera- tive models for image generation, namely BigGAN 22 GLIDE 2T,. VQDM g Stable Diffusion
V1.4 (25]. Stable Diffusion V1.5 (25), ADM 15), Midjoumny 20) and Wukong 35.. Each generator produces nearly the same number of images for each class, with 162 images for training and 6 for testing, with the exception of Stable Diffusion V1.5, which generates 166 images for training and for testing. combination of the fake images generated by a generator and their corresponding real images can be considered as subset, such as Stable Diffusion V1.4 subset. The real images are not shared across subsets. The number of generated images approximates the number of real images for the whole dataset and for each subset. The almost equal and high number of images generated by each generator in this dataset allows the properties of each generator to be fully explored when developing detection models without being affected by the imbalances in numbers. Our model input sentences follow the template "photo of class", with "class" " being substituted by ImageNet labels. For Wukong, Chinese sentences often achieve better generation quality. Thus, the sentences are translated into Chinese for the web API. For ADM and BIGGAN, we employ their pre-trained models on ImageNet, inputting the labels to generate images.
The generated images are shown in Figure [ More visualized images are provided in the supplemen- tary materials. It can be observed that overall the generated images are similar to the real images in ImageNet. Images with the same labels are analyzed in more detail. Animals and plants succeed in keeping the appearance of the target object consistent, e.g., samoyed with a similar appearance, and they differ in movement, perspective, and background. The appearance of the targets also varies for objects such as candles and lighthouses. Therefore, the generated images have a high degree of variability and reasonableness.
3.2 Fake Image Generators
Diffusion Model has recently achieved remarkable performances in image synthesis. Midjour- ney (20) is one of the most renowned commercial software programs, known for its exceptional image generation performance. We utilize Midjourney V5 for image generation, which offers more intricate details compared to previous versions, resulting in images that closely resemble real- world photographs. The resolution of images generated by Midjourney is 1024 x 1024. Wukong [35] is a large-scale eoxt-to-image generative model based on the diffusion model. This model is trained on the largest Chinese open-source multimodal dataset, the Wukong dataset, making it particularly suitable for Chinese language processing. The image resolution is 512 x 512. Stable Diffusion (25 is an advanced text-to-image diffusion model capable of generating highly realistic images based on any given text input. Stable Diffusion V1.4 is pretrained from the Stable Diffusion V1.2 check- point and fine-tuned on 225k steps at resolution 5 512x512 Leion-Assthetics V2 5+ dataset and drops 10% of the cext-conditioning. The training settings of Stable Diffusion V1.5 is the same as Stable Diffusion V1.4, except that Stable Diffusion V1.5 is fine-tuned on 595,000 steps. Both Stable Diffusion V1.4 and Stable Diffusion V1.5 generate images with a resolution of 512 x 512. ADM [5] proposes diffusion model which achieves better sample quality than GANs. We use model with classifier guidance, which is pretrained on ImageNet. GLIDE 21 is a diffusion model for text-conditional image synthesis. GLIDE uses text encoder to train a 3.5 billion diffusion model. We use classifier-free guidance for generating images. Its resolution is 256x256. VQDM g proposes latent-space method that eliminates the undirectional bias with previous methods and incorporates a mask- diffusion mechanism to alleviate the accumulation of errors. The resolution is 256x256.
GAN has brought significant quality improvements in image generation in the past decades. Big- GAN (2) is representative method in GAN family. BigGAN makes three contributions, including architectural changes, sampling techniques, and instabilities reduction techniques. We use the BigGAN model pretrained on ImageNet. The image resolution is 128 128.

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Table 2: Results of cross-validation on different training and testing subsets using ResNet-500.
Testing Subset
Avg
Training Subset
Midjourney
SD V1.4
SD
V1.5
ADM
GLIDE
Wukong
VQDM
BigGAN
Acc.
Midjourney
98.8
76.4
76.9
64.1
78.9
71.4
52.5
50.1
71.1
SD V1.4
54,9
99.9
99.7
53.5
61.9
98.2
56.6
52.0
72.1
SD V1.5
54.4
99.8
99.9
52.7
60.1
98.5
56.9
51.3
71.7
ADM
58.6
53.1
53.2
99.0
97.1
53.0
61.5
88.3
70.4
GLIDE
50.7
50.0
50.1
56.0
99.9
50.3
51.0
74.0
60.2
Wukong
54.5
99.7
99.6
51.4
58.3
99.9
58.7
50.9
71.6
VQDM
50.1
50.0
50.0
50.7
60.1
50.2
99.9
66.8
59.7
BigGAN
49.9
49.9
49.9
50.6
68.4
49.9
50.6
99.9
58.6
4 Genlmage Benchmark
4.1 Fake Image Detectors
In order to evaluate dataset, investigate and select some existing fake image detection methods.
Backbone Model can be directly utilized as the detector for the binary classification of real and fake images. We use ResNet-50 [9]. DeiT-S (28) and Swin-TI - [17 as the fake image detector. ResNet [9] based on convolutional neural network. DeïT-S [28] and Swin-T [17] are based on Transformer.
Without specific design for the fake image detection task, the backbone models can be considered baseline methods.
Fake Face Detector has been developed for long time. F3Net 23 proposes to simultancously explore frequency components partition and discrepancy of frequency statistics between real and fake images for face forgery detection. GramNet [18] uses global texture features to make fake face detection more robust and generalizable. These methods train models on face images. These models are difficult to directly work well with images beyond the face domain. However, these methods can still inspire the design of general image detectors.
General Fake Image Detector employs special designs for classifying general images getting rid of the limitation of face content. Spec (38) takes the frequency spectrum as input and synthesizes
GAN artifacts real images without the necessity of specific GAN models to generate fake images as training data. CNNSpot (31] uses ResNet-50 as binary classifier, with specific pre- and post- processing and data augmentation. However, the performance of existing methods on datasets comprising a combination of GAN and diffusion-
diffusion-generated generated images requires further improvement. Consequently, the development of detectors tailored to the unique characteristies of mixed GAN and diffusion data becomes imperative.
4.2 Task Cross-Generator Image Classification
We first evaluate the performance of the detector when trained and tested on images generated by the same generator. We perform our analysis with the most commonly used Resnet-50 (9] Our GenImage dataset consists of eight distinct subsets, each corresponding to specific generator. Within each subset, we further divide the data into training and testing sets, each comprising 1000 classes of images. As shown in Table training and testing within each subset consistently yield accuracy rates surpassing 98.5%. Notably, the Stable Diffusion V1.4 and Stable Diffusion V1.5 subsets achieve an exceptional accuracy of 99.9%. However, observe substantial performance degradation when training and testing are conducted using different generators. For instance, when ResNet-50 trained on Stable Diffusion V1.4 and tested on Midjourney, the binary classification accuracy drops to 54.9%.
Based on this observation, detecting fake image synthesized by specific generator is relatively straightforward, simply by training the binary classifier on dataset consisting of real images and fake images. However, this approach is likely to be tied to this generator and will not perform well on unknown generators. In real-world scenarios, the generator is often unknown at training. work, we hope to be able to effectively evaluate the generalization ability of the detector, i.e. the ability to distinguish between real and fake images independent the used generator. We propose the cross-generator image classification task to evaluate the recognition ability of the Al-generator detector. Table training on Stable Diffusion V1.4 achieves the best results. To this end, we train the model on Stable Diffusion V1.4 and subsequently test it the testing subsets from different
this

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Table 3: Results of different methods trained on SD V1.4 and evaluated on different testing subsets.
Testing Subset
Avg
Method
Midjournyy SD V1.4 (1.45.
SD V1.5
ADM GLIDE
Wukong VQDM BigGAN
Acc.
ResNet-500 9
54,9
99.9
99.7
53.5
61.9
98.2
56,6
52.0
72.1
DeïT-S 28
55.6
99.9
99.8
49.8
58.1
98.9
56.9
53.5
71.6
Swin-TT 17
62.1
99.9
99.8
49.8
67.6
99.1
62.3
57.6
74.8
CNNSpot [31
52.8
96.3
95.9
50.1
39.8
78.6
53.4
46.8
64.2
Spec [38
52.0
99.4
99.2
49.7
49.8
94.8
55.6
49.8
68.8
F3Net [23
50.1
99.9
99.9
49,9
50.0
99.9
49.9
49.9
68.7
GramNet [8
54.2
99.2
99.1
50.3
54.6
98.9
50.8
51.7
69.9
generators in this task. We then compute the average accuracy across the various test subsets, as shown in Table 3 In T for each method, we train one model on one generator and evaluate this model on eight generators. We average eight results for one method. Table for each method, we train eight models on eight generators respectively. We evaluate each model on eight generators. We average sixty-four results for one method. For example, we respectively train eight ResNet-50 models using eight generators and average their evaluation results Midjourney, yiclding an accuracy of 59.0%. The evaluation is also performed on the other generators, such as Stable Diffusion V1.4, which leads to another fifty-six evaluation results. All the sixty-four testing results are then averaged to achieve 66.9%. In summary, the cross- cross-generator generator training and evaluation contains two settings. The first setting is training model on SD V1.4, and evaluating the eight generators. The second setting We average the eight average accuracies in Table
the same as the training and evaluation in Table 2 to obtain the results of ResNet-50 in Table 4 Our evaluation offers comprehensive insights into the capabilities of the detectors. We conduct a thorough evaluation of various backbone architectures on dataset, including the CNN-based ResNer50 (). as well as the Transformer-batt DeiT-S 28 and Swin-T [17. This evaluation aims to assess the performance and effectiveness of these architectures in Al Al-generated image detection task. Additionally, we also evaluate the performance of exsiting Al- Al-generated image detectors, such as CNNSpot [31 and Spec (38).
In Table 3 and Table 4 current CNN-based models like ResNet perform similarly Transformer- based models like DeÎT-S and Swin-T. These backbone models share similar computation costs and parameters, and they are often used for 1000-class image classification ImageNet. For ImageNet 1000 class image classification, the backbone models tend to focus more on the classification of the content of the image. For GenImage, the focus is more the pattern of discriminating between real and fake images. We train them from scratch for binary classification. In the cross- generator image classification task, the best result is achieved by Swin-T, while the other two methods close in accuracy. Improving Transformer-basee methods is a promising direction in our dataset. We also evaluate the existing Al-generated image detectors, e.g., CNNSpot [3], and Spec (38). We train these models on our dataset and evaluate them. CNNSpot proposes that the recognition performance can be improved by augmenting the training data: (1) Images are blurred with a ~ Uniform(0.3] with 50% probability. (2) Images are JPEG-ed with 50% probability. GAN often causes unique artifacts in the up-sampling component. Spec show that such artifacts are manifested as replications of spectra in the frequency domain. Thus, Spec takes spectrum instead of pixel as its input for classification. CNNSpot and Spec work well on their GAN-based datasets. However, they perform even worse than the baseline backbone models on our dataset, mainly composed of images generated by the diffusion model. F3Net [23] contains two branches, namely Frequency-awaa Image Decomposition (FAD) and Local Frequency Statistics (LFS). FAD studies manipulation patterns through frequency-aware image decomposition. LFS extracts local frequency statistics. Besides, a mixed block is utilized for collaborative feature interaction. GramNet [18] observes the difference between fake and real faces. Then it leverages global texture features to enhance robustness and generalization. Both methods are inferior to training directly with binary classification backbone neural network model. Considering the advantages of all these approaches, designing a special backbone network could be a promising solution for GenImage.
4.3 Task 2: Degraded Image Classification
Images often encounter degradation problems during propagation 31 such as low resolution, compression, and noise interference. Detectors are supposed to robust to these challenges. To address this, propose evaluating the performance of the detector on these degraded images, which

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Table 4: Results of cross-validation on different training and test subsets using different methods. Eight models trained on eight generators are tested on one generator, and their average accuracy each data point in the testing subset column.
Testing Subset
Avg
Method
Midjournyy SD V1.4 SD
VI.5
ADM
GLIDE Wukong
VQDM BigGAN
Acc, (%)
ResNet-50
9
59.0
72.3
72.4
59.7
73.1
71.4
60.9
66.6
66.9
DeiT-S
60.7
74.2
74.2
59.5
71.1
73.1
61.7
66.3
67.6
Swin-T 17
61.7
76.0
76.1
61.3
76.9
75.1
65.8
69.5
70.3
CNNSpot [31
58.2
70.3
70.2
57.0
57.1
67.7
56.7
56.6
61.7
Spee 38
56.7
72,4
72.3
57.9
65,4
70.3
61.7
64.3
65.1
F3Net 23
55.1
73.1
73.1
66.5
57.8
72.3
62.1
56.5
64.6
GramNet 18
58.1
72.8
72.7
58.7
65.3
71.3
57.8
61.2
64.7
Table 5: Model evaluation degraded images. q denotes quality.
Testing Subset
Avg
Method
LR (112)
LR (64)
JPEG (Q=65)
JPEG (q=30)
Blur (a=3)
Blur (a=5)
Acc(%)
ResNet-50 9
96.2
57.4
51.9
51.2
97.9
69.4
70.6
DeïT-S 28
97.1
54.0
55.6
50.5
94.4
67.2
69.8
Swin-T 17
97.4
54.6
52.5
50.9
94.5
52.5
67.0
CNNSpot
50.0
50.0
97.3
97.3
97.4
77.9
78.3
Spec [38
50.0
49.9
50.8
50.4
49.9
F3Net 233
50.0
50.0
89.0
74.4
57.9
51.7
62.1
GramNet [18]
98.8
94.9
68.8
53.4
95,9
81.6
82.2
more accurately simulate practical conditions, as shown in Table After training the detector on our Stable Diffusion V1.4 subset, we employ series of methods to degrade only the testing set images. Specifically, we downsample the images to resolutions of 112 and 64 only in the testing set. Furthermore, utilize JPEG compression with quality ratios of 65 and 30, introducing compression artifacts. To introduce blurring effects, we incorporate Gaussian Blurring. As baseline models, ResNet-50, DeīT-S, and Swin-T all present similar results. They all detect images well for the low resolution at 112 x 112. However, for the lower resolution 64 x 64, they present poorer results. The experimental results suggest that these models these models also demonstrate similar performance. JPEG compression has a significant impact on these backbone models. It is worth noting that CNNSpot is robust both JPEG compression and Gaussian blurring, which is basically due to the fact that CNNSpot uses JPEG compression and Gaussian blurring additional data preprocessing during training. Therefore, designing reasonable preprocessing method is promising way to solve the degraded image classification problem in our dataset. By applying these detectors degraded images, we gain valuable insights into their performance under various challenging conditions. This evaluation provides a more comprehensive understanding of the capability of the detector handle practical image degradation.
sensitive to resolution. For Gaussian blurring,
GenImage Analysis
In this section, we conduct extensive experiments to investigate the characteristics of GenImage, and the effectiveness of this dataset.
5.1 Effect of Increasing the Number of Images
In general, increasing the scale of the dataset has been commonly believed to improve the performance of the classification model. However, this observation has not been fully explored in the fake image detection task. To assist the assessment of necessary data quantity, we conduct comprehensive experiments, as shown in T We conduct preliminary experiment using Stable Diffusion V1.4 generated images. In particular, our training different settings are introduced in the testing set. In identical- -eeerratr setting, the testing set is composed of 6000 fake images. On the other hand, in the cross-generator setting, the testing set contains 50000 fake images from 8 different generators. The same number of real images is collected classes, with each
is generated solely by Stable Diffusion V1.4. Two
from ImageNet 4. Firstly, we train a ResNet-50 on dataset consisting class containing 160 fake images. The number of real images is identical to the number of fake
7

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Table 6: Results of increasing the number of images.
Total
Image Classes
Images Per Class
Centtcal-Generaate Acc. (%)
Cross Cooss-eenrrtorr Acc. (%)
1600
10
160
73.6
60.9
1600
100
16
81.8
62.7
16000
100
160
97.7
68.7
16000
1000
96.3
68.4
162000
1000
162
99.9
72.1
Midjouney
SD V1.4
V1.5
ADM
ImageNet
GLIDE
Wukong
VQDM
BIgGAN
Figure 2: Frequency analysis on real images and generated images.
images. The accuracy achieved the identical- identical-generator testing set was only 73.6%, and even lower on the cross-generator testing set. We further expand our training set from two aspects: by increasing the number of classes and by increasing the number of images within each class. Encouragingly, we observe that the performance substantially improves with both approaches. This observation underscores the significance of augmenting the training set.
5.2 Frequency Analysis
we visualize the average spectral spectra of real images and fake images from different generators. Following CNNSpot [31]. generated by the generative model. The noise residuals R can be computed by the formula R = X - where X is the input image, and F(-) is the denoising filter DCNN 337 For each image source, we average the noise residuals of 1000 randomly selected images and use the Fourier transform of the results for spectral analysis. Comparing the real image, the image generated by the GAN, and the image generated by the Diffusion model, some interesting results can be discovered. shown in the form of regular grid. Real images from
In Figure
the discrete Fourier transform to investigate artifacts
The results show that for GAN, artifacts ImageNet contain few artifacts, as well as diffusion models. These results reflect that images from the diffusion models are closer the real image than BigGAN, and therefore diffusion models present a greater challenge for detection. Durall et al. 6 show that the up-sampling methods, such as the up-convolution or transposed convolution lead the inability of GANs correctly approximate the spectral distributions of training data, which explains why the artifacts occur in GANs. Richer et al. 24 observe that the diffusion models do not produce grid-like artifacts the frequency spectrum, but exhibit systematic mismatch towards higher frequencies. The hypothesis is that less weight is attached to the higher frequencies during training since matching lower frequencies is more important to the perceived quality of generated images.
5.3 Image Class Generalization
In order to verify that the detector trained on our dataset can well generalize to different contents of images, we sample three subsets of classes, i.e., 10, 50, and 100, for training ResNet-50. It can be seen that it is possible to generalize well other classes of images using only a subset of 1000 classes, and the more classes in the subset, the better the generalization performance. We use all the generators for training and testing. We first keep the number of training data constant, i.e., 12,800 images generated. The testing set contains 1000 classes of images with 50 generated

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images for each class. The training set and the testing set encompass the images generated by the eight generators. The number of real images in each class is identical that of the fake images. As shown in T training only subset of image classes can also achieve good results on 1000 classes of images, and more classes of images achieve higher accuracy. This result validates the generalization performance of our dataset over image classes, which also implies training 1000 classes achieve better sults on unseen images. We also compare the accuracy under two settings: 100 classes of im- ages with 128 images for each class and 100 classes images with 1300 images for each class.
Table 7: Results of increasing the class.
Image Classes
Fake Images Per Class
Acc.(%)
10
1280
78.8
50
256
80.3
100
128
81.2
100
1300
98.4
can be seen that increasing the number of images for each class improves the accuracy.
5.4 Generator Correlation Analysis
As the generator category is not commonly available as prior knowledge, we conduct an extensive evaluation on the rross-generator image classification task, encompassing eight different generative methods, as shown in Figure 3 ResNet-50 is the detector. It can be seen that the same generator achieves optimal performance in both training and testing. Generators exhibiting higher similarity tend to yield superior cross-generator performance. For instance, Stable Diffusion V1.4 4 and Stable Diffusion V V1.5, sharing similar architecture, demonstrate good classification results when trained on the former and tested the latter. Likewise, the detection model trained on Stable Diffusion models can effectively generalize to Wukong. From Figure (a), we can see that training on Stable Diffusion V1.4, Stable Diffusion V1.5, and Wukong yields the best overall generalization performance. Midjourney poses the greatest challenge in terms generalization. Figure 3 (a) the accuracy score map, which shows the accuracies with different training and testing approaches. In Figure (b), we compute Pearson correlation based on the accuracy score map. Pearson correlation coefficient is measure linear correlation between two sets of data. Figure (b) models with similar architectures tend to exhibit higher relevance, such as Stable Diffusion V1.4, Stable Diffusion V1.5, and Wukong.
(b) highlights that
141
1.00
Traiiin Approsch
SDV12
Training Approachs
S0V13
0.75
0.50
ADM
0:25
0.1
Webing
Wäreg
VQUM
RgdAN
Rydo
VL4
201 VLS
Ad5
viguu
RiyoAN
Mideame
20 99.4
Ada
1rou
Testing Approach
Testing Approach
Accuracy score map
(b) Accuracy correlation map
Figure 3: visualization of generator correlations.
5.5 Image Content Generalization
Our dataset encompasses a broad range of image classes, extending beyond the conventional lim- itations of face and art images. As demonstrated in Table our dataset exhibits notable content generalization capability. Specifically, a ResNet-50 trained on our dataset effectively works on face and art images. LFW [1I] is publicly available database of face images designed for face recognition problem. We have assembled 10,000 face images from the LFW dataset, comple- mented by an equal number of images generated using face labels from the same dataset. Laion-Art subset of Laion-5B [26]. Several lightweight models estimate how people would rate each image in Laion-S 5B based aesthetics, and the images with high scores kept for Laion-Art.

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DiffusionDB 33 is a suge-ssale gallery dataset with 1.8 million unique prompts. We have accumulated 10,000
Table 8: Image content generalization.
art images from Laion-Art, alongside 10,000 art images generated using prompts from DiffusionDB, as shown in For training, we employ the Stable Diffusion V1.4 subset of Genlmage. Stable Diffusion V1.4 is
Image
Image Source
Acc.
Content
Real
Fake
(%)
Figure +
Face
LFW
SD VI.4
99.9
Art
Laion-Art
SD V1.4
95.0
also used to generate the face and art fake images. We directly evaluate our model on the testing dataset without finetuning. As shown by the table, our dataset exhibits commendable generalization capacity, achieving over 95.0% accuracy in identifying face and art images.
Real
Fake
Art
Face
Figure 4: The visualization of the face images and art images.
Conclusion
This paper introduces GenImage. dataset specifically designed for detecting fake images generated by generative models. Genlmage serves as a million-scale benchmark, surpassing previous datasets and benchmarks in terms of the number images, image content, and generator selection. We propose two tasks, namely cross- goss-generrtoor image classification and degraded image classification, to assess the detection performance of existing detectors on Genlmage. Additionally, a detailed analysis of the dataset is provided, enabling us to gain insights into how Genlmage can contribute to the development of detectors for real-world applications.
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12


Reviews:
[{"review_id": "l2ySPGMQFK", "reviewer": "Reviewer_27YU", "summary": "", "questions": "", "limitations": "The limitations section is brief and could be expanded. I would appreciate discussion of the issue of adversaries, and limitations in terms of generalization to other generator architectures (beyond diffusion models and GANs).", "rating": 6, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "* Larger scale / more diverse than prior fake image detection datasets, which were either small-scale or focused just on faces.\n* Includes recent diffusion models.\n* The analysis of generalization (across generators and across image categories) is valuable.\n* A timely contribution, as fake images are now ubiquitous on the Internet and there is large social interest in detecting them.", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}, {"review_id": "kRz0m0vauO", "reviewer": "Reviewer_APC2", "summary": "", "questions": "", "limitations": "I think their contribution is limited: \n1. Collecting such a fake image detection dataset is not a particularly difficult task, although it requires many GPU resources and will facilitate researchers' research. In my opinion, the contribution level of this dataset can only reach the level of a boardline accept.\n2. The other limitation is that the datasets  focus on 8 types of models only, but in the future, there might be many other models coming out and this dataset approach doesn't seem scalable. The popular IF[1], CogView[2], for instance, is missing.\n3. (Mentioned in Opportunities For Improvement) Based on the generation model, we can generate more fake images than just 1M (LAION has **2B** real images). Maybe more than releasing the dataset, it could make sense to release a modular code that users can use to generate more data using distributed inference with different generation models.\n4. (Mentioned in Opportunities For Improvement) The author did not mention some important hyperparameters used for generation, such as guidance scale, sampling method and steps of diffusion, and seed.\n\n[1] CogView: Mastering Text-to-Image Generation via Transformers\n\n[2] https://github.com/deep-floyd/IF/tree/develop", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "This paper has several notable strengths:\n1. The writing is clear and well-structured. The authors state clearly what they aim to do, and this paper is easy to follow. The problem description is well done, as are description of experiments, which enhances the accessibility and accountability of the paper. \n2. The article proposes a large dataset called GenImage, which contains one million fake images generated by eight different models. The authors have benchmarked several base models on two evaluation settings: Cross-Generator Image Classification and Degraded Image Classification. The use of this dataset will enable researchers to effectively develop and evaluate novel fake image detectors.\n", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}, {"review_id": "8khdxTWtgW", "reviewer": "Reviewer_GjRx", "summary": "", "questions": "", "limitations": "\n\nThe paper does not provide a detailed analysis of the performance of different generative models in generating fake images. It is unclear how the performance of the proposed dataset would be affected if different generative models were used.\n\nThe paper only evaluates the performance of existing fake image detectors on the proposed dataset, without proposing any new fake image detection methods. It would be interesting to see if the proposed dataset can inspire the development of more effective fake image detectors.\n\nThe paper does not provide a detailed discussion of the limitations of the proposed dataset. For example, it is unclear how the proposed dataset would perform in detecting fake images generated by new or unknown generative models.", "rating": 6, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "\nThe paper makes some significant contributions to the field of fake image detection, including:\n\nThe construction of a large-scale benchmark dataset with a diverse range of image categories and state-of-the-art generative models, which can facilitate the development and evaluation of fake image detectors.\n\nThe proposal of two evaluation tasks, including cross-generator image classification and degraded image classification, which can simulate real-world scenarios and provide a more comprehensive evaluation of fake image detectors.\n\nThe comprehensive analysis of the dataset using existing state-of-the-art fake image detectors, which can serve as a baseline for future research.", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}, {"review_id": "ZgsCWkOWSI", "reviewer": "Reviewer_ZWev", "summary": "", "questions": "", "limitations": "the generation of images by single classes mostly produces images with one central object (=class lable). This could introduce some limitation of generality as more complex scenes are not present. especially for the high-res images, it would be possible to generate more complex, multi object content...  ", "rating": 9, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": " The dataset addresses an important topic with potentially brought impact and closes the large gap to existing benchmarks which where mostly missing diverse data from Diffusion models. The provided analysis shows, that in contrast to GAN data, current detection models fail to transfer from and to Diffusion Models. this makes it even more urgent for the community to have such a large and diverse benchmark.", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}]

Author rebuttal:

Meta-review: This paper introduces a new dataset for AI-generated image detection. This dataset is used to benchmark several fake image detectors.
The dataset addresses an important and hot topic. 
The proposal has some limitations, pointed out by the reviewers, but overall the evaluation is positive.
Decision: Accept (Poster)