# Privacy protection based on mask template

arXiv: https://arxiv.org/abs/2202.06250 · HTML: https://mlsanity.com/paper/2202.06250/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 39 references that are in the map.
- First posted on arXiv 13 Feb 2022.

## Builds on (most cited first, 23 of 23)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain](https://mlsanity.com/paper/1708.06733/) (2017)
- [Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning](https://mlsanity.com/paper/1712.05526/) (2017)
- [Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networks](https://mlsanity.com/paper/1805.12185/) (2018)
- [Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks](https://mlsanity.com/paper/1804.00792/) (2018)
- [Machine Learning Models that Remember Too Much](https://mlsanity.com/paper/1709.07886/) (2017)
- [AdvHat: Real-world adversarial attack on ArcFace Face ID system](https://mlsanity.com/paper/1908.08705/) (2019)
- [Neural Trojans](https://mlsanity.com/paper/1710.00942/) (2017)
- [Adversarial Frontier Stitching for Remote Neural Network Watermarking](https://mlsanity.com/paper/1711.01894/) (2017)
- [Technical Report: When Does Machine Learning FAIL? Generalized Transferability for Evasion and Poisoning Attacks](https://mlsanity.com/paper/1803.06975/) (2018)
- [Data Poisoning against Differentially-Private Learners: Attacks and Defenses](https://mlsanity.com/paper/1903.09860/) (2019)
- [Poisoning Attacks to Graph-Based Recommender Systems](https://mlsanity.com/paper/1809.04127/) (2018)
- [Generative Poisoning Attack Method Against Neural Networks](https://mlsanity.com/paper/1703.01340/) (2017)
- [Digital Watermarking for Deep Neural Networks](https://mlsanity.com/paper/1802.02601/) (2018)
- [DeepMarks: A Digital Fingerprinting Framework for Deep Neural Networks](https://mlsanity.com/paper/1804.03648/) (2018)
- [FireCaffe: near-linear acceleration of deep neural network training on compute clusters](https://mlsanity.com/paper/1511.00175/) (2015)
- [Indirect Adversarial Attacks via Poisoning Neighbors for Graph Convolutional Networks](https://mlsanity.com/paper/2002.08012/) (2020)
- [Fawkes: Protecting Privacy against Unauthorized Deep Learning Models](https://mlsanity.com/paper/2002.08327/) (2020)
- [PoTrojan: powerful neural-level trojan designs in deep learning models](https://mlsanity.com/paper/1802.03043/) (2018)
- [Have You Stolen My Model? Evasion Attacks Against Deep Neural Network Watermarking Techniques](https://mlsanity.com/paper/1809.00615/) (2018)
- [Are You Tampering With My Data?](https://mlsanity.com/paper/1808.06809/) (2018)

## Abstract (arXiv)

Powerful recognition algorithms are widely used in the Internet or important medical systems, which poses a serious threat to personal privacy. Although the law provides for diversity protection, e.g. The General Data Protection Regulation (GDPR) in Europe and Articles 1032 to 1039 of the civil code in China. However, as an important privacy disclosure event, biometric data is often hidden, which is difficult for the owner to detect and trace to the source. Human biometrics generally exist in images. In order to avoid the disclosure of personal privacy, we should prevent unauthorized recognition algorithms from acquiring the real features of the original image.