# A Study of the Human Perception of Synthetic Faces

arXiv: https://arxiv.org/abs/2111.04230 · HTML: https://mlsanity.com/paper/2111.04230/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 43 references that are in the map.
- First posted on arXiv 8 Nov 2021.

## Builds on (most cited first, 19 of 19)

- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [FaceForensics++: Learning to Detect Manipulated Facial Images](https://mlsanity.com/paper/1901.08971/) (2019)
- [FSGAN: Subject Agnostic Face Swapping and Reenactment](https://mlsanity.com/paper/1908.05932/) (2019)
- [AttGAN: Facial Attribute Editing by Only Changing What You Want](https://mlsanity.com/paper/1711.10678/) (2017)
- [Media Forensics and DeepFakes: an overview](https://mlsanity.com/paper/2001.06564/) (2020)
- [Artificial Fingerprinting for Generative Models: Rooting Deepfake Attribution in Training Data](https://mlsanity.com/paper/2007.08457/) (2020)
- [FakeCatcher: Detection of Synthetic Portrait Videos using Biological Signals](https://mlsanity.com/paper/1901.02212/) (2019)
- [STGAN: A Unified Selective Transfer Network for Arbitrary Image Attribute Editing](https://mlsanity.com/paper/1904.09709/) (2019)
- [FaceForensics: A Large-scale Video Dataset for Forgery Detection in Human Faces](https://mlsanity.com/paper/1803.09179/) (2018)
- [On Face Segmentation, Face Swapping, and Face Perception](https://mlsanity.com/paper/1704.06729/) (2017)
- [Face Aging With Conditional Generative Adversarial Networks](https://mlsanity.com/paper/1702.01983/) (2017)
- [Geometry Guided Adversarial Facial Expression Synthesis](https://mlsanity.com/paper/1712.03474/) (2017)
- [Synthesis of High-Quality Visible Faces from Polarimetric Thermal Faces using Generative Adversarial Networks](https://mlsanity.com/paper/1812.05155/) (2018)
- [More Real than Real: A Study on Human Visual Perception of Synthetic Faces](https://mlsanity.com/paper/2106.07226/) (2021)
- [SREFI: Synthesis of Realistic Example Face Images](https://mlsanity.com/paper/1704.06693/) (2017)
- [Face Aging with Contextual Generative Adversarial Nets](https://mlsanity.com/paper/1802.00237/) (2018)
- [Attributes Aware Face Generation with Generative Adversarial Networks](https://mlsanity.com/paper/2012.01782/) (2020)

## Built on by (most cited first, 14 of 14)

- [SFace: Privacy-friendly and Accurate Face Recognition using Synthetic Data](https://mlsanity.com/paper/2206.10520/) (2022)
- [CYBORG: Blending Human Saliency Into the Loss Improves Deep Learning](https://mlsanity.com/paper/2112.00686/) (2021)
- [Testing Human Ability To Detect Deepfake Images of Human Faces](https://mlsanity.com/paper/2212.05056/) (2022)
- [AI-Generated Faces in the Real World: A Large-Scale Case Study of Twitter Profile Images](https://mlsanity.com/paper/2404.14244/) (2024)
- [The Value of AI Guidance in Human Examination of Synthetically-Generated Faces](https://mlsanity.com/paper/2208.10544/) (2022)
- [Unmasking Illusions: Understanding Human Perception of Audiovisual Deepfakes](https://mlsanity.com/paper/2405.04097/) (2024)
- [Adversarial Robustness of AI-Generated Image Detectors in the Real World](https://mlsanity.com/paper/2410.01574/) (2024)
- [Pose Impact Estimation on Face Recognition using 3D-Aware Synthetic Data with Application to Quality Assessment](https://mlsanity.com/paper/2303.00491/) (2023)
- [Analysis of Human Perception in Distinguishing Real and AI-Generated Faces: An Eye-Tracking Based Study](https://mlsanity.com/paper/2409.15498/) (2024)
- [A Collaboration Strategy in the Mining Pool for Proof-of-Neural-Architecture Consensus](https://mlsanity.com/paper/2206.07089/) (2022)
- [Training Better Deep Learning Models Using Human Saliency](https://mlsanity.com/paper/2410.16190/) (2024)
- [Only Train Once: Uncertainty-Aware One-Class Learning for Face Authenticity Detection](https://mlsanity.com/paper/2605.10040/) (2026)
- [Can You Trust What You See? Human and AI Detection of Synthetic Legal Evidence](https://mlsanity.com/paper/2606.07613/) (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)

Advances in face synthesis have raised alarms about the deceptive use of synthetic faces. Can synthetic identities be effectively used to fool human observers? In this paper, we introduce a study of the human perception of synthetic faces generated using different strategies including a state-of-the-art deep learning-based GAN model. This is the first rigorous study of the effectiveness of synthetic face generation techniques grounded in experimental techniques from psychology. We answer important questions such as how often do GAN-based and more traditional image processing-based techniques confuse human observers, and are there subtle cues within a synthetic face image that cause humans to perceive it as a fake without having to search for obvious clues? To answer these questions, we conducted a series of large-scale crowdsourced behavioral experiments with different sources of face imagery. Results show that humans are unable to distinguish synthetic faces from real faces under several different circumstances. This finding has serious implications for many different applications where face images are presented to human users.