文献汇总｜AI生成图像检测相关数据集汇总

前言本博客汇总当前AI生成图像检测领域用到的数据集及相关链接。⚠️ 更新说明由于博客与 Synthetic Image Research Map 的内容存在较多重叠分别维护两套整理结果会带来较高的更新成本因此本博客后续将不再单独更新相关论文与数据集列表。更完整、持续更新的生成图像研究整理请访问https://meiling-fdu.github.io/synthetic-image-research-map/web/?datasetpreview. 如需查看与数据集和基准相关的论文请在页面的筛选栏中选择 Paper Type → Datasets / Benchmarks。Research Map 中的信息将作为后续主要维护版本。 相关文章链接工具分享Synthetic Image Research MapAI 生成图像检测与溯源研究的交互式文献地图目录20202022202320242020CNNSpothttps://github.com/peterwang512/CNNDetectionTestset: The zip file contains images from 13 CNN-based synthesis algorithms, including the 12 testsets from the paper and images downloaded from whichfaceisreal.com. Images from each algorithm are stored in a separate folder. In each category, real images are in the 0_real folder, and synthetic images are in the 1_fake folder.Note: ProGAN, StyleGAN, StyleGAN2, CycleGAN testset contains multiple classes, which are stored in separate subdirectories.Training set: The training set used in the paper can be downloaded here (Try alternative links 1,2 if the previous link does not work). All images are from LSUN or generated by ProGAN, and they are separated in 20 object categories. Similarly, in each category, real images are in the 0_real folder, and synthetic images are in the 1_fake folder.Validation set: The validation set consists of held-out ProGAN real and fake images, and can be downloaded here. The directory structure is identical to that of the training set.2022IEEE VIP Cup2022 IEEE Video and Image Processing Cup | Synthetic Image Detection Challenge)https://grip-unina.github.io/vipcup2022/SAChttps://github.com/JD-P/simulacra-aesthetic-captions数据集中图像命名包含生成所需的提示词如0_An_artwork_of_a_broken_wine_bottle_in_the_medium_of_dry_pigments_1.png43044_…png此外该数据集也被用于美学质量评价。2023DiffusionForensicshttps://github.com/ZhendongWang6/DIREDMimageDetectionhttps://github.com/grip-unina/DMimageDetection/tree/main/training_codehttps://luminohope.org/pub/publication/arxiv_diffusion_detection_2022/GenImagehttps://github.com/GenImage-Dataset/GenImageWe employ eight generative models for image generation, namely BigGAN [2], GLIDE [21], VQDM [8], Stable Diffusion V1.4 [25], Stable Diffusion V1.5 [25], ADM [5], Midjourney [20], and Wukong [35].Fake2Mhttps://arxiv.org/pdf/2304.13023We constructed 3 training fake datasets with about 2M images, named Fake2M, and 11 validation fake datasets with about 257K images using different latest modern generative models, which contain the SOTA Diffusion models (Stable Diffusion [46], IF [4]), the SOTA GAN model (StyleGAN3 [31]), the SOTA autoregressive model (CogView2 [19]), and the SOTA generative model (Midjounrey [6]), as shown in Tab. 2. We describe the details of our datasets in the following subsections.TWIGMAhttps://yiqunchen.github.io/TWIGMA/index.html#datasetArtiFacthttps://github.com/awsaf49/artifactTo include a diverse collection of real images from multiple categories, including Human/Human Faces, Animal/Animal Faces, Places, Vehicles, Art, and many other real-life objects, the proposed dataset utilizes 8 sources [7], [14]–[16] that are carefully chosen. Additionally, to inject diversity in terms of generators, the proposed dataset synthesizes images from 25 distinct methods [7]–[9], [14]–[24]. Specifically, it includes 13 GANs, 7 Diffusion, and 5 other miscellaneous generators. On the other hand, in terms of syntheticity, there are 20 fully manipulating and 5 partially manipulating generators, thus providing a broad spectrum of diversity in terms of generators used. The distribution of real and fake data with different sources is shown in Fig.1 and Fig.2, respectively. The dataset contains a total of 2,496,738 images, comprising 964,989 real images and 1,531,749 fake images. The most frequently occurring categories in the dataset are Human/Human Faces, Animal/Animal Faces, Vehicles, Places, and Art.13GANs: BigGAN, CycleGAN, Denoising Diffusion GAN, Diffusion GAN, FaceSynthetics, GANformer, GauGAN, ProGAN, ProjectedGAN, StarGAN, StyleGAN1, StyleGAN2, StyleGAN37DMs: DDPM, Glide, LaMa, Latent Diffusion, Stable Diffusion, Taming Transformer, VQDiffusion5 Others: CIPS, Generative Inpainting, MAT, Palette, SFHQSynthbusterhttps://zenodo.org/records/10066460UniversarialFakeDetecthttps://github.com/WisconsinAIVision/UniversalFakeDetect11GANs 7 DMs 1 其他DiffusionDBhttps://github.com/poloclub/diffusiondbWe construct DIFFUSIONDB (Fig. 2) by scraping user-generated images from the official Stable Diffusion Discord server. We choose Stable Diffusion as it is currently the only open-source large text-to-image generative model, and all generated images have a CC0 1.0 license that allows uses for any purposeCiFAKEhttps://github.com/jordan-bird/CIFAKE-Real-and-AI-Generated-Synthetic-ImagesCIFAKE is a dataset that contains 60,000 synthetically-generated images and 60,000 real images (collected from CIFAR-10). For the FAKE images, we generated the equivalent of CIFAR-10 with Stable Diffusion version 1.4LASTEDhttps://github.com/HighwayWu/LASTED训练集生成模型ProGANLexicaStable Diffusion测试集DreamBooth, Midjourney, NightCafe, StalbeAI, YiJian蚁鉴DDDB 未公开https://arxiv.org/abs/2302.14475DeepArt 未公开https://export.arxiv.org/pdf/2312.10407DEFAKE 未公开https://github.com/zeyangsha/De-Fake20k real image for training 10k real images for testing2024COCOFakehttps://github.com/aimagelab/COCOFakeCOCOFake, containing about 1.2 million images generated from the original COCO image–caption pairs using two recent text-to-image diffusion models, namely Stable Diffusion v1.4 and v2.0.FOSIDhttps://github.com/mever-team/fosidhttps://zenodo.org/records/13648239D^3https://aimagelab.ing.unimore.it/imagelab/page.asp?IdPage57The Diffusion-generated Deepfake Detection (D3) Dataset is a comprehensive collection designed for large-scale deepfake detection. It includes 9.2 million generated images, created using four state-of-the-art diffusion model generators. Each image is generated based on realistic textual descriptions from the LAION-400M dataset.We generate a comprehensive dataset that focuses on images generated by diffusion models and encompasses a collection of 9.2 million images produced by using four different generators.Generators: Stable Diffusion 1.4, Stable Diffusion 2.1, Stable Diffusion XL, and DeepFloyd IFConsequently, we generate and release the Diffusion-generated Deepfake Detection (D3 ) dataset containing 2.3 million records, each composed of a real image coming from LAION-400M [44] dataset and images from four generators, for a total of 9.2 million generated images. To verify the generation capabilities of deepfake detection methods to unseen generators, we also collect a challenging test set composed of 4.8k real images, each paired with 12 fake images generated by as many diffusion-based generators.With the aim of increasing the variance of the dataset, images have been generated with different aspect ratios, i.e. 256x256, 512x512, 640×480, and 640×360. Moreover, to mimic the distribution of real images, we also employ a variety of encoding and compression methods (BMP, GIF, JPEG, TIFF, PNG). In particular, we closely follow the distribution of encoding methods of LAION itself, therefore favoring the presence of JPEG-encoded images.ImagiNethttps://github.com/delyan-boychev/imaginethttps://huggingface.co/datasets/delyanboychev/imaginetTo support the development of defensive methods, we introduce ImagiNet, a high-resolution and balanced dataset for synthetic image detection, designed to mitigate potential biases in existing resources. It contains 200k examples, spanning four content categories: photos, paintings, faces, and uncategorized. Synthetic images are produced with open-source and proprietary generators, whereas real counterparts of the same content type are collected from public datasets.AntifakePrompthttps://github.com/nctu-eva-lab/AntifakePromptWe conduct full-spectrum experiments on datasets from a diversity of 3 held-in and 20 held-out generative models, covering modern text-to-image generation, image editing and adversarial image attacks.Real datasets. We use Microsoft COCO (COCO) (Lin et al. 2014) dataset and Flickr30k (Young et al. 2014) dataset. In our work, we selected 90K images, with shorter sides greater than 224, from COCO dataset for the real images in the training dataset. Moreover, to assess the generalizability of our method over various real images, we additionally select 3K images from Flickr30k dataset to form a held-out testing dataset, adhering to the same criterion of image size. 93kFake image for training 150kfor testing3k*21 63kFakeBenchhttps://arxiv.org/abs/2404.13306Regarding the genuine images, we sample 3,000 images from ImageNet [76] and DIV2K dataset [77].COCOXGENhttps://github.com/heikeadel/cocoxgenCOCOXGENCOCO Extended With Generated Images, which consists of real photos from the COCO dataset as well as images generated with SDXL and Fooocus using prompts of two standardized lengths.WildRFhttps://github.com/barcavia/RealTime-DeepfakeDetection-in-the-RealWorldWe propose to improve deepfake evaluation and align it with real-world settings by introducing WildRF, a realistic benchmark consisting of images sourced from popular social platforms. Specifically, we manually collected real images and fake images using keywords and hashtags associated with the suitable content. Our protocol is to train on one platform (e.g., Reddit) and test the detector on real and fake images from other unseen platforms (e.g., Twitter and Facebook).WildFakePaper: https://arxiv.org/pdf/2402.11843Dataset: https://modelscope.cn/datasets/hy2628982280/WildFakeLSUNDBhttps://github.com/jonasricker/diffusion-model-deepfake-detectionThe main dataset used in this work is hosted on Zenodo. In total, the dataset contains 50k samples (256x256) for each of the following generators trained on LSUN Bedroom, divided into train, validation, and test set (39k/1k/10k).DIFhttps://sergo2020.github.io/DIF/