# How to Distinguish AI-Generated Images from Authentic Photographs

arXiv: https://arxiv.org/abs/2406.08651 · HTML: https://mlsanity.com/paper/2406.08651/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models.
- First posted on arXiv 12 Jun 2024.

## Placed in

- Diffusion models / [Image generation, text-to-image and editing](https://mlsanity.com/diffusion/image-generation-text-to-image-and-editing/)

## Built on by (most cited first, 12 of 12)

- [Deepfake-Eval-2024: A Multi-Modal In-the-Wild Benchmark of Deepfakes Circulated in 2024](https://mlsanity.com/paper/2503.02857/) (2025)
- [AIGI-Holmes: Towards Explainable and Generalizable AI-Generated Image Detection via Multimodal Large Language Models](https://mlsanity.com/paper/2507.02664/) (2025)
- [Characterizing Photorealism and Artifacts in Diffusion Model-Generated Images](https://mlsanity.com/paper/2502.11989/) (2025)
- [A Crack in the Bark: Leveraging Public Knowledge to Remove Tree-Ring Watermarks](https://mlsanity.com/paper/2506.10502/) (2025)
- [TruthLens:A Training-Free Paradigm for DeepFake Detection](https://mlsanity.com/paper/2503.15342/) (2025)
- [A Practical Synthesis of Detecting AI-Generated Textual, Visual, and Audio Content](https://mlsanity.com/paper/2504.02898/) (2025)
- [Designing Effective Digital Literacy Interventions for Boosting Deepfake Discernment](https://mlsanity.com/paper/2507.23492/) (2025)
- [A Study of Commonsense Reasoning over Visual Object Properties](https://mlsanity.com/paper/2508.10956/) (2025)
- [Visual Persuasion: What Influences Decisions of Vision-Language Models?](https://mlsanity.com/paper/2602.15278/) (2026)
- [Generative AI Literacy Training Improves Intelligence Analysts' Discrimination of Real and AI-Generated Images](https://mlsanity.com/paper/2606.28510/) (2026)
- [When Composition Doesn't Add Up: Humans Identifying Defects in AI-Generated Images](https://mlsanity.com/paper/2608.25933/) (2026)
- [Calibrating LLM Explanations to Human Decisions with Reinforcement Learning](https://mlsanity.com/paper/2610.17306/) (2026)

## Abstract (arXiv)

The high level of photorealism in state-of-the-art diffusion models like Midjourney, Stable Diffusion, and Firefly makes it difficult for untrained humans to distinguish between real photographs and AI-generated images. To address this problem, we designed a guide to help readers develop a more critical eye toward identifying artifacts, inconsistencies, and implausibilities that often appear in AI-generated images. The guide is organized into five categories of artifacts and implausibilities: anatomical, stylistic, functional, violations of physics, and sociocultural. For this guide, we generated 138 images with diffusion models, curated 9 images from social media, and curated 42 real photographs. These images showcase the kinds of cues that prompt suspicion towards the possibility an image is AI-generated and why it is often difficult to draw conclusions about an image's provenance without any context beyond the pixels in an image. Human-perceptible artifacts are not always present in AI-generated images, but this guide reveals artifacts and implausibilities that often emerge. By drawing attention to these kinds of artifacts and implausibilities, we aim to better equip people to distinguish AI-generated images from real photographs in the future.