ArtiFact: A Large-Scale Dataset with Artificial and Factual Images for Generalizable and Robust Synthetic Image Detection [ICIP 2023]

Paper:

IEEE Xplore: https://ieeexplore.ieee.org/document/10222083
ArXiv: https://arxiv.org/abs/2302.11970

Abstract: Synthetic image generation has opened up new opportunities but has also created threats in regard to privacy, authenticity, and security. Detecting fake images is of paramount importance to prevent illegal activities, and previous research has shown that generative models leave unique patterns in their synthetic images that can be exploited to detect them. However, the fundamental problem of generalization remains, as even state-of-the-art detectors encounter difficulty when facing generators never seen during training. To assess the generalizability and robustness of synthetic image detectors in the face of real-world impairments, this paper presents a large-scale dataset named ArtiFact, comprising diverse generators, object categories, and real-world challenges. Moreover, the proposed multi-class classification scheme, combined with a filter stride reduction strategy addresses social platform impairments and effectively detects synthetic images from both seen and unseen generators. The proposed solution significantly outperforms other top teams by 8.34% on Test 1, 1.26% on Test 2, and 15.08% on Test 3 in the IEEE VIP Cup challenge at ICIP 2022, as measured by the accuracy metric.

Presentation:

Visual Summary:

Update

[22 June 2023] - The work has been accepted to IEEE ICIP 2023 conference.

Result on IEEE VIP Cup at ICIP 2022

Accuracy (%) of Top3 Teams on Leaderboard,

| Team Names | Test 1 | Test 2 | Test 3 | | :-------------------- | :--------: | :--------: | :--------: | | Sherlock | 87.70 | 77.52 | 73.45 | | FAU Erlangen-Nürnberg | 87.14 | 81.74 | 75.52 | | Megatron (Ours) | 96.04 | 83.00 | 90.60 |

Note: A small portion of the proposed ArtiFact dataset, totaling 222K images of 71K real images and 151K fake images from only 13 generators is used in the IEEE VIP Cup. Here all the Test data is kept confidential from all participating teams. Additionally, the generators used for the Test 1 data are known to all teams, whereas the generators for Test 2 and Test 3 are kept undisclosed.

Dataset Description

Total number of images: $2,496,738$
Number of real images: $964,989$
Number of fake images: $1,531,749$
Number of generators used for fake images: $25$ (including $13$ GANs, $7$ Diffusion, and $5$ miscellaneous generators)
Number of sources used for real images: $8$
Categories included in the dataset: Human/Human Faces, Animal/Animal Faces, Places, Vehicles, Art, and other real-life objects
Image Resolution: $200 \times 200$

Data Distribution

Real

Fake

Download Dataset

The dataset is hosted on Kaggle. The dataset can be downloaded i) directly from the browser using the link below or ii) can be downloaded using kaggle-api.

i) Directly from Browser

Link: ArtiFact Dataset

ii) Kaggle API

!kaggle datasets download -d awsaf49/artifact-dataset

How to Use

The dataset is organized into folders, each of which corresponds to a specific generator of synthetic images or source of real images. Each folder contains a metadata.csv file, which provides information about the images in the folder. It contains following columns,

image_path : The relative path of the image file.
target : The label for the image, which is either 0 for real or 1 for fake.
category : The category (cat or dog etc) of the image

Data Generation

Images are randomly sampled from different methods then transformed using impairments. The methods are listed below,
<details close> <summary>Methods</summary>
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | :-------------- | :--------------------------------------------- | :----------------------------------------- | :---------------------------------- | :-------------------------------------------- | :--------------------------------------------- | :------------------------------------------------- | :---------------------------------------------------------------- | :------------------------------------------------ | :---------------------------------------------- | :------------------------------------------ | :------------------------------------------ | :----------------------------------------------------------- | :---------------------------------------------------- | :----------------------------------------- | :---------------------------------------- | :----------------------------------------------- | :------------------------------------------------ | :-------------------------------------------------------------------------- | :----------------------------------------- | :---------------------------------------------------------------------------------------- | :---------------------------------------- | :---------------------------------------- | :---------------------------------------------------------------------------- | :-------------------------------------------- | :----------------------------------------------------------- | :-------------------------------------------------- | :-------------------------------------------------- | :-------------------------------------------------------- | :------------------------------------------------ | :----------------------------------------------------- | :-------------------------------------- | :---------------------------------------------------------------- | :-------------------------------------------------------------- | | Method | ImageNet | COCO | LSUN | AFHQ | FFHQ | Metfaces | CelebAHQ | Landscape | Glide | StyleGAN2 | StyleGAN3 | Generative Inpainting | Taming Transformer | MAT | LaMa | Stable Diffusion | VQ Diffusion | Palette | StyleGAN1 | Latent Diffusion | CIPS | StarGAN | BigGAN | GANformer | ProjectedGAN | SFHQ | FaceSynthetics | Denoising Diffusion GAN | DDPM | DiffusionGAN | GauGAN | ProGAN | CycleGAN | | Reference | link | link | [li

Artifact

Install / Use

README