# Organic or Diffused: Can We Distinguish Human Art from AI-generated Images?

arXiv: https://arxiv.org/abs/2402.03214 · HTML: https://mlsanity.com/paper/2402.03214/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 82 references that are in the map.
- First posted on arXiv 5 Feb 2024.

## Builds on (most cited first, 27 of 27)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [CNN-generated images are surprisingly easy to spot... for now](https://mlsanity.com/paper/1912.11035/) (2019)
- [DIRE for Diffusion-Generated Image Detection](https://mlsanity.com/paper/2303.09295/) (2023)
- [The Curse of Recursion: Training on Generated Data Makes Models Forget](https://mlsanity.com/paper/2305.17493/) (2023)
- [GenImage: A Million-Scale Benchmark for Detecting AI-Generated Image](https://mlsanity.com/paper/2306.08571/) (2023)
- [Scaling Language-Image Pre-training via Masking](https://mlsanity.com/paper/2212.00794/) (2022)
- [On the detection of synthetic images generated by diffusion models](https://mlsanity.com/paper/2211.00680/) (2022)
- [Self-Consuming Generative Models Go MAD](https://mlsanity.com/paper/2307.01850/) (2023)
- [Glaze: Protecting Artists from Style Mimicry by Text-to-Image Models](https://mlsanity.com/paper/2302.04222/) (2023)
- [DE-FAKE: Detection and Attribution of Fake Images Generated by Text-to-Image Generation Models](https://mlsanity.com/paper/2210.06998/) (2022)
- [Raising the Bar of AI-generated Image Detection with CLIP](https://mlsanity.com/paper/2312.00195/) (2023)
- [Towards the Detection of Diffusion Model Deepfakes](https://mlsanity.com/paper/2210.14571/) (2022)
- [CIFAKE: Image Classification and Explainable Identification of AI-Generated Synthetic Images](https://mlsanity.com/paper/2303.14126/) (2023)
- [Large Language Models Suffer From Their Own Output: An Analysis of the Self-Consuming Training Loop](https://mlsanity.com/paper/2311.16822/) (2023)
- [Seeing is not always believing: Benchmarking Human and Model Perception of AI-Generated Images](https://mlsanity.com/paper/2304.13023/) (2023)
- [Robustness and Generalizability of Deepfake Detection: A Study with Diffusion Models](https://mlsanity.com/paper/2309.02218/) (2023)
- [R-LPIPS: An Adversarially Robust Perceptual Similarity Metric](https://mlsanity.com/paper/2307.15157/) (2023)
- [A Representative Study on Human Detection of Artificially Generated Media Across Countries](https://mlsanity.com/paper/2312.05976/) (2023)
- [Detecting Generated Images by Real Images Only](https://mlsanity.com/paper/2311.00962/) (2023)
- [Testing Human Ability To Detect Deepfake Images of Human Faces](https://mlsanity.com/paper/2212.05056/) (2022)
- [D4: Detection of Adversarial Diffusion Deepfakes Using Disjoint Ensembles](https://mlsanity.com/paper/2202.05687/) (2022)

## Built on by (most cited first, 21 of 21)

- [Characterizing Photorealism and Artifacts in Diffusion Model-Generated Images](https://mlsanity.com/paper/2502.11989/) (2025)
- [On the Difficulty of Constructing a Robust and Publicly-Detectable Watermark](https://mlsanity.com/paper/2502.04901/) (2025)
- [Examining the Prevalence and Dynamics of AI-Generated Media in Art Subreddits](https://mlsanity.com/paper/2410.07302/) (2024)
- [Detecting Origin Attribution for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2403.19653/) (2024)
- [AI-generated Image Detection: Passive or Watermark?](https://mlsanity.com/paper/2411.13553/) (2024)
- [EvoGuard: An Extensible Agentic RL-based Framework for Practical and Evolving AI-Generated Image Detection](https://mlsanity.com/paper/2603.17343/) (2026)
- [ASAP: Interpretable Analysis and Summarization of AI-generated Image Patterns at Scale](https://mlsanity.com/paper/2404.02990/) (2024)
- [The Adversarial AI-Art: Understanding, Generation, Detection, and Benchmarking](https://mlsanity.com/paper/2404.14581/) (2024)
- [AnimeDL-2M: Million-Scale AI-Generated Anime Image Detection and Localization in Diffusion Era](https://mlsanity.com/paper/2504.11015/) (2025)
- [Can VLMs Detect and Localize Fine-Grained AI-Edited Images?](https://mlsanity.com/paper/2505.15644/) (2025)
- [AI-Generated Image Detectors Overrely on Global Artifacts: Evidence from Inpainting Exchange](https://mlsanity.com/paper/2602.00192/) (2026)
- [MANTA -- Model Adapter Native generations that's Affordable](https://mlsanity.com/paper/2409.14363/) (2024)
- [Chameleon: Benchmarking Detection and Backtracking on Commercial-Grade AI-Generated Videos](https://mlsanity.com/paper/2503.06624/) (2025)
- [On Semiotic-Grounded Interpretive Evaluation of Generative Art](https://mlsanity.com/paper/2604.08641/) (2026)
- [AI-Generated Images: What Humans and Machines See When They Look at the Same Image](https://mlsanity.com/paper/2605.06143/) (2026)
- [From Celebrities to Anyone: Characterizing AI Nudification Content, Technology, and Community Dynamics on 4chan](https://mlsanity.com/paper/2606.27234/) (2026)
- [Abductive Corroboration of Probabilistic AI Models for Forensic Synthetic Media Detection](https://mlsanity.com/paper/2607.05434/) (2026)
- [VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection](https://mlsanity.com/paper/2607.06254/) (2026)
- [Robustness of AI-Art Detectors under Generator Shift](https://mlsanity.com/paper/2608.11643/) (2026)
- [ASAP: Visual Analytics for Identifying and Analyzing Image Patterns in AI-generated Images](https://mlsanity.com/paper/2609.27371/) (2026)
- [Trustworthy synthetic visual media: Evidence across the media lifecycle](https://mlsanity.com/paper/2609.34232/) (2026)

## Abstract (arXiv)

The advent of generative AI images has completely disrupted the art world. Distinguishing AI generated images from human art is a challenging problem whose impact is growing over time. A failure to address this problem allows bad actors to defraud individuals paying a premium for human art and companies whose stated policies forbid AI imagery. It is also critical for content owners to establish copyright, and for model trainers interested in curating training data in order to avoid potential model collapse. There are several different approaches to distinguishing human art from AI images, including classifiers trained by supervised learning, research tools targeting diffusion models, and identification by professional artists using their knowledge of artistic techniques. In this paper, we seek to understand how well these approaches can perform against today's modern generative models in both benign and adversarial settings. We curate real human art across 7 styles, generate matching images from 5 generative models, and apply 8 detectors (5 automated detectors and 3 different human groups including 180 crowdworkers, 4000+ professional artists, and 13 expert artists experienced at detecting AI). Both Hive and expert artists do very well, but make mistakes in different ways (Hive is weaker against adversarial perturbations while Expert artists produce higher false positives). We believe these weaknesses will remain as models continue to evolve, and use our data to demonstrate why a combined team of human and automated detectors provides the best combination of accuracy and robustness.