# More Real than Real: A Study on Human Visual Perception of Synthetic Faces

arXiv: https://arxiv.org/abs/2106.07226 · HTML: https://mlsanity.com/paper/2106.07226/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 16 references that are in the map.
- First posted on arXiv 14 Jun 2021.

## Builds on (most cited first, 8 of 8)

- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Face2Face: Real-time Face Capture and Reenactment of RGB Videos](https://mlsanity.com/paper/2007.14808/) (2020)
- [Media Forensics and DeepFakes: an overview](https://mlsanity.com/paper/2001.06564/) (2020)
- [FaceForensics: A Large-scale Video Dataset for Forgery Detection in Human Faces](https://mlsanity.com/paper/1803.09179/) (2018)
- [Reconstructing Faces from fMRI Patterns using Deep Generative Neural Networks](https://mlsanity.com/paper/1810.03856/) (2018)
- [Image-to-Video Generation via 3D Facial Dynamics](https://mlsanity.com/paper/2105.14678/) (2021)

## Built on by (most cited first, 23 of 23)

- [Intriguing properties of synthetic images: from generative adversarial networks to diffusion models](https://mlsanity.com/paper/2304.06408/) (2023)
- [Detecting GAN-generated Images by Orthogonal Training of Multiple CNNs](https://mlsanity.com/paper/2203.02246/) (2022)
- [FakeBench: Probing Explainable Fake Image Detection via Large Multimodal Models](https://mlsanity.com/paper/2404.13306/) (2024)
- [GAN-generated Faces Detection: A Survey and New Perspectives](https://mlsanity.com/paper/2202.07145/) (2022)
- [Towards Universal GAN Image Detection](https://mlsanity.com/paper/2112.12606/) (2021)
- [A Representative Study on Human Detection of Artificially Generated Media Across Countries](https://mlsanity.com/paper/2312.05976/) (2023)
- [A Study of the Human Perception of Synthetic Faces](https://mlsanity.com/paper/2111.04230/) (2021)
- [Characteristics and prevalence of fake social media profiles with AI-generated faces](https://mlsanity.com/paper/2401.02627/) (2024)
- [Characterizing Photorealism and Artifacts in Diffusion Model-Generated Images](https://mlsanity.com/paper/2502.11989/) (2025)
- [Open-Eye: An Open Platform to Study Human Performance on Identifying AI-Synthesized Faces](https://mlsanity.com/paper/2205.06680/) (2022)
- [Single-Model Attribution of Generative Models Through Final-Layer Inversion](https://mlsanity.com/paper/2306.06210/) (2023)
- [AI-Generated Faces in the Real World: A Large-Scale Case Study of Twitter Profile Images](https://mlsanity.com/paper/2404.14244/) (2024)
- [TrueFake: A Real World Case Dataset of Last Generation Fake Images also Shared on Social Networks](https://mlsanity.com/paper/2504.20658/) (2025)
- [PetroGAN: A novel GAN-based approach to generate realistic, label-free petrographic datasets](https://mlsanity.com/paper/2204.05114/) (2022)
- [Sequential training of GANs against GAN-classifiers reveals correlated "knowledge gaps" present among independently trained GAN instances](https://mlsanity.com/paper/2303.15533/) (2023)
- [ForamViT-GAN: Exploring New Paradigms in Deep Learning for Micropaleontological Image Analysis](https://mlsanity.com/paper/2304.04291/) (2023)
- [Analysis of Human Perception in Distinguishing Real and AI-Generated Faces: An Eye-Tracking Based Study](https://mlsanity.com/paper/2409.15498/) (2024)
- [Synthetic Art Generation and DeepFake Detection A Study on Jamini Roy Inspired Dataset](https://mlsanity.com/paper/2503.23226/) (2025)
- [Advance Fake Video Detection via Vision Transformers](https://mlsanity.com/paper/2504.20669/) (2025)
- [Backbone is All You Need: Assessing Vulnerabilities of Frozen Foundation Models in Synthetic Image Forensics](https://mlsanity.com/paper/2605.13381/) (2026)
- [Can You Trust What You See? Human and AI Detection of Synthetic Legal Evidence](https://mlsanity.com/paper/2606.07613/) (2026)
- [Generative AI Literacy Training Improves Intelligence Analysts' Discrimination of Real and AI-Generated Images](https://mlsanity.com/paper/2606.28510/) (2026)
- [Frontier vision-language models have overtaken young adults at detecting AI-generated portraits -- but not their calibration](https://mlsanity.com/paper/2608.30210/) (2026)

## Abstract (arXiv)

Deep fakes became extremely popular in the last years, also thanks to their increasing realism. Therefore, there is the need to measures human's ability to distinguish between real and synthetic face images when confronted with cutting-edge creation technologies. We describe the design and results of a perceptual experiment we have conducted, where a wide and diverse group of volunteers has been exposed to synthetic face images produced by state-of-the-art Generative Adversarial Networks (namely, PG-GAN, StyleGAN, StyleGAN2). The experiment outcomes reveal how strongly we should call into question our human ability to discriminate real faces from synthetic ones generated through modern AI.