# Landmark Breaker: Obstructing DeepFake By Disturbing Landmark Extraction

arXiv: https://arxiv.org/abs/2102.00798 · HTML: https://mlsanity.com/paper/2102.00798/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 38 references that are in the map.
- First posted on arXiv 1 Feb 2021.

## Builds on (most cited first, 28 of 28)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [DeepFool: a simple and accurate method to fool deep neural networks](https://mlsanity.com/paper/1511.04599/) (2015)
- [Adversarial Machine Learning at Scale](https://mlsanity.com/paper/1611.01236/) (2016)
- [The Limitations of Deep Learning in Adversarial Settings](https://mlsanity.com/paper/1511.07528/) (2015)
- [Universal adversarial perturbations](https://mlsanity.com/paper/1610.08401/) (2016)
- [Improving Transferability of Adversarial Examples with Input Diversity](https://mlsanity.com/paper/1803.06978/) (2018)
- [Celeb-DF: A Large-scale Challenging Dataset for DeepFake Forensics](https://mlsanity.com/paper/1909.12962/) (2019)
- [How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)](https://mlsanity.com/paper/1703.07332/) (2017)
- [Face X-ray for More General Face Forgery Detection](https://mlsanity.com/paper/1912.13458/) (2019)
- [MesoNet: a Compact Facial Video Forgery Detection Network](https://mlsanity.com/paper/1809.00888/) (2018)
- [Exposing DeepFake Videos By Detecting Face Warping Artifacts](https://mlsanity.com/paper/1811.00656/) (2018)
- [Adversarial Examples for Semantic Segmentation and Object Detection](https://mlsanity.com/paper/1703.08603/) (2017)
- [Pose Guided Person Image Generation](https://mlsanity.com/paper/1705.09368/) (2017)
- [Look at Boundary: A Boundary-Aware Face Alignment Algorithm](https://mlsanity.com/paper/1805.10483/) (2018)
- [Multi-task Learning For Detecting and Segmenting Manipulated Facial Images and Videos](https://mlsanity.com/paper/1906.06876/) (2019)
- [On the Robustness of Semantic Segmentation Models to Adversarial Attacks](https://mlsanity.com/paper/1711.09856/) (2017)
- [Adversarial Attacks Beyond the Image Space](https://mlsanity.com/paper/1711.07183/) (2017)
- [Disrupting Deepfakes: Adversarial Attacks Against Conditional Image Translation Networks and Facial Manipulation Systems](https://mlsanity.com/paper/2003.01279/) (2020)
- [In Ictu Oculi: Exposing AI Generated Fake Face Videos by Detecting Eye Blinking](https://mlsanity.com/paper/1806.02877/) (2018)
- [Towards Imperceptible and Robust Adversarial Example Attacks against Neural Networks](https://mlsanity.com/paper/1801.04693/) (2018)
- [Adversarial Examples for Semantic Image Segmentation](https://mlsanity.com/paper/1703.01101/) (2017)
- [Aggregation via Separation: Boosting Facial Landmark Detector with Semi-Supervised Style Translation](https://mlsanity.com/paper/1908.06440/) (2019)
- [Foveation-based Mechanisms Alleviate Adversarial Examples](https://mlsanity.com/paper/1511.06292/) (2015)
- [Hiding Faces in Plain Sight: Disrupting AI Face Synthesis with Adversarial Perturbations](https://mlsanity.com/paper/1906.09288/) (2019)
- [Facial Landmarks Detection by Self-Iterative Regression based Landmarks-Attention Network](https://mlsanity.com/paper/1803.06598/) (2018)

## Built on by (most cited first, 5 of 5)

- [TAFIM: Targeted Adversarial Attacks against Facial Image Manipulations](https://mlsanity.com/paper/2112.09151/) (2021)
- [Restricted Black-box Adversarial Attack Against DeepFake Face Swapping](https://mlsanity.com/paper/2204.12347/) (2022)
- [Boosting Active Defense Persistence: A Two-Stage Defense Framework Combining Interruption and Poisoning Against Deepfake](https://mlsanity.com/paper/2508.07795/) (2025)
- [Detection of Real-time DeepFakes in Video Conferencing with Active Probing and Corneal Reflection](https://mlsanity.com/paper/2210.14153/) (2022)
- [Scapegoat Generation for Privacy Protection from Deepfake](https://mlsanity.com/paper/2303.02930/) (2023)

## Abstract (arXiv)

The recent development of Deep Neural Networks (DNN) has significantly increased the realism of AI-synthesized faces, with the most notable examples being the DeepFakes. The DeepFake technology can synthesize a face of target subject from a face of another subject, while retains the same face attributes. With the rapidly increased social media portals (Facebook, Instagram, etc), these realistic fake faces rapidly spread though the Internet, causing a broad negative impact to the society. In this paper, we describe Landmark Breaker, the first dedicated method to disrupt facial landmark extraction, and apply it to the obstruction of the generation of DeepFake videos.Our motivation is that disrupting the facial landmark extraction can affect the alignment of input face so as to degrade the DeepFake quality. Our method is achieved using adversarial perturbations. Compared to the detection methods that only work after DeepFake generation, Landmark Breaker goes one step ahead to prevent DeepFake generation. The experiments are conducted on three state-of-the-art facial landmark extractors using the recent Celeb-DF dataset.