# Robust Face-Swap Detection Based on 3D Facial Shape Information

arXiv: https://arxiv.org/abs/2104.13665 · HTML: https://mlsanity.com/paper/2104.13665/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 23 references that are in the map.
- First posted on arXiv 28 Apr 2021.

## Builds on (most cited first, 14 of 14)

- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [FaceForensics++: Learning to Detect Manipulated Facial Images](https://mlsanity.com/paper/1901.08971/) (2019)
- [Face X-ray for More General Face Forgery Detection](https://mlsanity.com/paper/1912.13458/) (2019)
- [FSGAN: Subject Agnostic Face Swapping and Reenactment](https://mlsanity.com/paper/1908.05932/) (2019)
- [Exposing Deep Fakes Using Inconsistent Head Poses](https://mlsanity.com/paper/1811.00661/) (2018)
- [The Creation and Detection of Deepfakes: A Survey](https://mlsanity.com/paper/2004.11138/) (2020)
- [DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection](https://mlsanity.com/paper/2001.00179/) (2020)
- [The Deepfake Detection Challenge (DFDC) Preview Dataset](https://mlsanity.com/paper/1910.08854/) (2019)
- [Two-Stream Neural Networks for Tampered Face Detection](https://mlsanity.com/paper/1803.11276/) (2018)
- [Recurrent Convolutional Strategies for Face Manipulation Detection in Videos](https://mlsanity.com/paper/1905.00582/) (2019)
- [Detecting GAN-generated Imagery using Color Cues](https://mlsanity.com/paper/1812.08247/) (2018)
- [Detecting Deep-Fake Videos from Appearance and Behavior](https://mlsanity.com/paper/2004.14491/) (2020)

## Built on by (most cited first, 2 of 2)

- [A Dataless FaceSwap Detection Approach Using Synthetic Images](https://mlsanity.com/paper/2212.02571/) (2022)
- [M3D-Net: Multi-Modal 3D Facial Feature Reconstruction Network for Deepfake Detection](https://mlsanity.com/paper/2604.14574/) (2026)

## Abstract (arXiv)

Maliciously-manipulated images or videos - so-called deep fakes - especially face-swap images and videos have attracted more and more malicious attackers to discredit some key figures. Previous pixel-level artifacts based detection techniques always focus on some unclear patterns but ignore some available semantic clues. Therefore, these approaches show weak interpretability and robustness. In this paper, we propose a biometric information based method to fully exploit the appearance and shape feature for face-swap detection of key figures. The key aspect of our method is obtaining the inconsistency of 3D facial shape and facial appearance, and the inconsistency based clue offers natural interpretability for the proposed face-swap detection method. Experimental results show the superiority of our method in robustness on various laundering and cross-domain data, which validates the effectiveness of the proposed method.