# ArtiFact: A Large-Scale Dataset with Artificial and Factual Images for Generalizable and Robust Synthetic Image Detection

arXiv: https://arxiv.org/abs/2302.11970 · HTML: https://mlsanity.com/paper/2302.11970/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 27 references that are in the map.
- First posted on arXiv 23 Feb 2023.

## Builds on (most cited first, 20 of 20)

- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [Taming Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2012.09841/) (2020)
- [Palette: Image-to-Image Diffusion Models](https://mlsanity.com/paper/2111.05826/) (2021)
- [Alias-Free Generative Adversarial Networks](https://mlsanity.com/paper/2106.12423/) (2021)
- [StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation](https://mlsanity.com/paper/1711.09020/) (2017)
- [Diffusion Models in Vision: A Survey](https://mlsanity.com/paper/2209.04747/) (2022)
- [CNN-generated images are surprisingly easy to spot... for now](https://mlsanity.com/paper/1912.11035/) (2019)
- [ImageNet-21K Pretraining for the Masses](https://mlsanity.com/paper/2104.10972/) (2021)
- [Free-Form Image Inpainting with Gated Convolution](https://mlsanity.com/paper/1806.03589/) (2018)
- [Leveraging Frequency Analysis for Deep Fake Image Recognition](https://mlsanity.com/paper/2003.08685/) (2020)
- [GAN Inversion: A Survey](https://mlsanity.com/paper/2101.05278/) (2021)
- [On the detection of synthetic images generated by diffusion models](https://mlsanity.com/paper/2211.00680/) (2022)
- [MAT: Mask-Aware Transformer for Large Hole Image Inpainting](https://mlsanity.com/paper/2203.15270/) (2022)
- [Fake It Till You Make It: Face analysis in the wild using synthetic data alone](https://mlsanity.com/paper/2109.15102/) (2021)
- [Do GANs leave artificial fingerprints?](https://mlsanity.com/paper/1812.11842/) (2018)
- [Are GAN generated images easy to detect? A critical analysis of the state-of-the-art](https://mlsanity.com/paper/2104.02617/) (2021)
- [Image Generators with Conditionally-Independent Pixel Synthesis](https://mlsanity.com/paper/2011.13775/) (2020)
- [RepMix: Representation Mixing for Robust Attribution of Synthesized Images](https://mlsanity.com/paper/2207.02063/) (2022)
- [A survey on GANs for computer vision: Recent research, analysis and taxonomy](https://mlsanity.com/paper/2203.11242/) (2022)

## Built on by (most cited first, 30 of 35)

- [A Sanity Check for AI-generated Image Detection](https://mlsanity.com/paper/2406.19435/) (2024)
- [SIDA: Social Media Image Deepfake Detection, Localization and Explanation with Large Multimodal Model](https://mlsanity.com/paper/2412.04292/) (2024)
- [Detecting Multimedia Generated by Large AI Models: A Survey](https://mlsanity.com/paper/2402.00045/) (2024)
- [Detecting Images Generated by Deep Diffusion Models using their Local Intrinsic Dimensionality](https://mlsanity.com/paper/2307.02347/) (2023)
- [WildFake: A Large-scale Challenging Dataset for AI-Generated Images Detection](https://mlsanity.com/paper/2402.11843/) (2024)
- [AI-Face: A Million-Scale Demographically Annotated AI-Generated Face Dataset and Fairness Benchmark](https://mlsanity.com/paper/2406.00783/) (2024)
- [GenVidBench: A 6-Million Benchmark for AI-Generated Video Detection](https://mlsanity.com/paper/2501.11340/) (2025)
- [Semi-Truths: A Large-Scale Dataset of AI-Augmented Images for Evaluating Robustness of AI-Generated Image detectors](https://mlsanity.com/paper/2411.07472/) (2024)
- [Methods and Trends in Detecting AI-Generated Images: A Comprehensive Review](https://mlsanity.com/paper/2502.15176/) (2025)
- [AI-GenBench: A New Ongoing Benchmark for AI-Generated Image Detection](https://mlsanity.com/paper/2504.20865/) (2025)
- [WILD: a new in-the-Wild Image Linkage Dataset for synthetic image attribution](https://mlsanity.com/paper/2504.19595/) (2025)
- [E3: Ensemble of Expert Embedders for Adapting Synthetic Image Detectors to New Generators Using Limited Data](https://mlsanity.com/paper/2404.08814/) (2024)
- [A Critical Assessment of Modern Generative Models' Ability to Replicate Artistic Styles](https://mlsanity.com/paper/2502.15856/) (2025)
- [Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples](https://mlsanity.com/paper/2509.25682/) (2025)
- [Local Statistics for Generative Image Detection](https://mlsanity.com/paper/2310.16684/) (2023)
- [Premonition: Using Generative Models to Preempt Future Data Changes in Continual Learning](https://mlsanity.com/paper/2403.07356/) (2024)
- [Multiverse Through Deepfakes: The MultiFakeVerse Dataset of Person-Centric Visual and Conceptual Manipulations](https://mlsanity.com/paper/2506.00868/) (2025)
- [AiGen-FoodReview: A Multimodal Dataset of Machine-Generated Restaurant Reviews and Images on Social Media](https://mlsanity.com/paper/2401.08825/) (2024)
- [The Adversarial AI-Art: Understanding, Generation, Detection, and Benchmarking](https://mlsanity.com/paper/2404.14581/) (2024)
- [RSFAKE-1M: A Large-Scale Dataset for Detecting Diffusion-Generated Remote Sensing Forgeries](https://mlsanity.com/paper/2505.23283/) (2025)
- ["Humor, Art, or Misinformation?": A Multimodal Dataset for Intent-Aware Synthetic Image Detection](https://mlsanity.com/paper/2508.20670/) (2025)
- [Generalized Design Choices for Deepfake Detectors](https://mlsanity.com/paper/2511.21507/) (2025)
- [TextRich: A Multi-Domain Benchmark for Detecting AI-Generated Text-Rich Images from GPT-Image-2](https://mlsanity.com/paper/2606.19259/) (2026)
- [DailyBench: A Unified Benchmark for AI-Generated and Manipulated Images from Modern Generative Models](https://mlsanity.com/paper/2607.24016/) (2026)
- [Deepfakes: we need to re-think the concept of "real" images](https://mlsanity.com/paper/2509.21864/) (2025)
- [Mirage: Unveiling Hidden Artifacts in Synthetic Images with Large Vision-Language Models](https://mlsanity.com/paper/2510.03840/) (2025)
- [From Masks to Pixels and Meaning: A New Taxonomy, Benchmark, and Metrics for VLM Image Tampering](https://mlsanity.com/paper/2603.20193/) (2026)
- [Towards More Accurate Fake Detection on Images Generated from Advanced Generative and Neural Rendering Models](https://mlsanity.com/paper/2411.08642/) (2024)
- [Robustness in AI-Generated Detection: Enhancing Resistance to Adversarial Attacks](https://mlsanity.com/paper/2505.03435/) (2025)
- [R2BD: A Reconstruction-Based Method for Generalizable and Efficient Detection of Fake Images](https://mlsanity.com/paper/2601.08867/) (2026)

## Abstract (arXiv)

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.