# On the Robustness of Face Recognition Algorithms Against Attacks and Bias

arXiv: https://arxiv.org/abs/2002.02942 · HTML: https://mlsanity.com/paper/2002.02942/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Feb 2020.

## Built on by (most cited first, 16 of 16)

- [A Comprehensive Study on Face Recognition Biases Beyond Demographics](https://mlsanity.com/paper/2103.01592/) (2021)
- [Biometrics: Trust, but Verify](https://mlsanity.com/paper/2105.06625/) (2021)
- [Fooling the Eyes of Autonomous Vehicles: Robust Physical Adversarial Examples Against Traffic Sign Recognition Systems](https://mlsanity.com/paper/2201.06192/) (2022)
- [Controllable Inversion of Black-Box Face Recognition Models via Diffusion](https://mlsanity.com/paper/2303.13006/) (2023)
- [Adversarial Relighting Against Face Recognition](https://mlsanity.com/paper/2108.07920/) (2021)
- [Robustness Disparities in Commercial Face Detection](https://mlsanity.com/paper/2108.12508/) (2021)
- [Feature and Label Embedding Spaces Matter in Addressing Image Classifier Bias](https://mlsanity.com/paper/2110.14336/) (2021)
- [An Examination of Bias of Facial Analysis based BMI Prediction Models](https://mlsanity.com/paper/2204.10262/) (2022)
- [Adventures of Trustworthy Vision-Language Models: A Survey](https://mlsanity.com/paper/2312.04231/) (2023)
- [Robustness Disparities in Face Detection](https://mlsanity.com/paper/2211.15937/) (2022)
- [A large-scale study of performance and equity of commercial remote identity verification technologies across demographics](https://mlsanity.com/paper/2409.12318/) (2024)
- [Are Commercial Face Detection Models as Biased as Academic Models?](https://mlsanity.com/paper/2201.10047/) (2022)
- [Are Face Detection Models Biased?](https://mlsanity.com/paper/2211.03588/) (2022)
- [Investigating Fairness of Ocular Biometrics Among Young, Middle-Aged, and Older Adults](https://mlsanity.com/paper/2110.01641/) (2021)
- [Race Bias Analysis of Bona Fide Errors in face anti-spoofing](https://mlsanity.com/paper/2210.05366/) (2022)
- [Mask-up: Investigating Biases in Face Re-identification for Masked Faces](https://mlsanity.com/paper/2402.13771/) (2024)

## Abstract (arXiv)

Face recognition algorithms have demonstrated very high recognition performance, suggesting suitability for real world applications. Despite the enhanced accuracies, robustness of these algorithms against attacks and bias has been challenged. This paper summarizes different ways in which the robustness of a face recognition algorithm is challenged, which can severely affect its intended working. Different types of attacks such as physical presentation attacks, disguise/makeup, digital adversarial attacks, and morphing/tampering using GANs have been discussed. We also present a discussion on the effect of bias on face recognition models and showcase that factors such as age and gender variations affect the performance of modern algorithms. The paper also presents the potential reasons for these challenges and some of the future research directions for increasing the robustness of face recognition models.