# Exposing GAN-synthesized Faces Using Landmark Locations

arXiv: https://arxiv.org/abs/1904.00167 · HTML: https://mlsanity.com/paper/1904.00167/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Mar 2019.

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

- [Preserving Fairness Generalization in Deepfake Detection](https://mlsanity.com/paper/2402.17229/) (2024)
- [Can ChatGPT Detect DeepFakes? A Study of Using Multimodal Large Language Models for Media Forensics](https://mlsanity.com/paper/2403.14077/) (2024)
- [Improving the Efficiency and Robustness of Deepfakes Detection through Precise Geometric Features](https://mlsanity.com/paper/2104.04480/) (2021)
- [Countering Malicious DeepFakes: Survey, Battleground, and Horizon](https://mlsanity.com/paper/2103.00218/) (2021)
- [Eyes Tell All: Irregular Pupil Shapes Reveal GAN-generated Faces](https://mlsanity.com/paper/2109.00162/) (2021)
- [GAN-generated Faces Detection: A Survey and New Perspectives](https://mlsanity.com/paper/2202.07145/) (2022)
- [Robust Attentive Deep Neural Network for Exposing GAN-generated Faces](https://mlsanity.com/paper/2109.02167/) (2021)
- [Exposing Deepfake Face Forgeries with Guided Residuals](https://mlsanity.com/paper/2205.00753/) (2022)
- [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)
- [The Deepfake Detection Dilemma: A Multistakeholder Exploration of Adversarial Dynamics in Synthetic Media](https://mlsanity.com/paper/2102.06109/) (2021)
- [Detection of Deepfake Videos Using Long Distance Attention](https://mlsanity.com/paper/2106.12832/) (2021)
- [Finding AI-Generated Faces in the Wild](https://mlsanity.com/paper/2311.08577/) (2023)
- [LOTR: Face Landmark Localization Using Localization Transformer](https://mlsanity.com/paper/2109.10057/) (2021)
- [COMICS: End-to-end Bi-grained Contrastive Learning for Multi-face Forgery Detection](https://mlsanity.com/paper/2308.01520/) (2023)
- [Face Forgery Detection Based on Facial Region Displacement Trajectory Series](https://mlsanity.com/paper/2212.03678/) (2022)
- [Vulnerability of Automatic Identity Recognition to Audio-Visual Deepfakes](https://mlsanity.com/paper/2311.17655/) (2023)
- [Rethinking Individual Fairness in Deepfake Detection](https://mlsanity.com/paper/2507.14326/) (2025)
- [Fake-image detection with Robust Hashing](https://mlsanity.com/paper/2102.01313/) (2021)
- [Detecting High-Quality GAN-Generated Face Images using Neural Networks](https://mlsanity.com/paper/2203.01716/) (2022)
- [Pneumonia Detection in Chest X-Ray Images : Handling Class Imbalance](https://mlsanity.com/paper/2301.08479/) (2023)
- [Fighting Malicious Media Data: A Survey on Tampering Detection and Deepfake Detection](https://mlsanity.com/paper/2212.05667/) (2022)
- [ForensicsForest Family: A Series of Multi-scale Hierarchical Cascade Forests for Detecting GAN-generated Faces](https://mlsanity.com/paper/2308.00964/) (2023)
- [Sub-pixel face landmarks using heatmaps and a bag of tricks](https://mlsanity.com/paper/2103.03059/) (2021)
- [Spritz-PS: Validation of Synthetic Face Images Using a Large Dataset of Printed Documents](https://mlsanity.com/paper/2304.02982/) (2023)
- [Multi-Channel Cross Modal Detection of Synthetic Face Images](https://mlsanity.com/paper/2311.16773/) (2023)
- [StegaFFD: Privacy-Preserving Face Forgery Detection via Fine-Grained Steganographic Domain Lifting](https://mlsanity.com/paper/2603.02886/) (2026)
- [Is Seeing Believing? Evaluating Human Sensitivity to Synthetic Video](https://mlsanity.com/paper/2603.13846/) (2026)
- [Multi-Tool Image Editing Attribution in Facial Forgery](https://mlsanity.com/paper/2609.02751/) (2026)

## Abstract (arXiv)

Generative adversary networks (GANs) have recently led to highly realistic image synthesis results. In this work, we describe a new method to expose GAN-synthesized images using the locations of the facial landmark points. Our method is based on the observations that the facial parts configuration generated by GAN models are different from those of the real faces, due to the lack of global constraints. We perform experiments demonstrating this phenomenon, and show that an SVM classifier trained using the locations of facial landmark points is sufficient to achieve good classification performance for GAN-synthesized faces.