# Unravelling Robustness of Deep Learning based Face Recognition Against Adversarial Attacks

arXiv: https://arxiv.org/abs/1803.00401 · HTML: https://mlsanity.com/paper/1803.00401/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Feb 2018.

## Built on by (most cited first, 30 of 31)

- [Advances in adversarial attacks and defenses in computer vision: A survey](https://mlsanity.com/paper/2108.00401/) (2021)
- [Distributional Robustness Loss for Long-tail Learning](https://mlsanity.com/paper/2104.03066/) (2021)
- [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)
- [Pixel-Level Face Image Quality Assessment for Explainable Face Recognition](https://mlsanity.com/paper/2110.11001/) (2021)
- [Unravelling the Effect of Image Distortions for Biased Prediction of Pre-trained Face Recognition Models](https://mlsanity.com/paper/2108.06581/) (2021)
- [Verification of Neural Networks' Global Robustness](https://mlsanity.com/paper/2402.19322/) (2024)
- [Explaining Adversarial Vulnerability with a Data Sparsity Hypothesis](https://mlsanity.com/paper/2103.00778/) (2021)
- [Boosting Robustness Verification of Semantic Feature Neighborhoods](https://mlsanity.com/paper/2209.05446/) (2022)
- [Secure and Trustworthy Artificial Intelligence-Extended Reality (AI-XR) for Metaverses](https://mlsanity.com/paper/2210.13289/) (2022)
- [TAIGen: Training-Free Adversarial Image Generation via Diffusion Models](https://mlsanity.com/paper/2508.15020/) (2025)
- [Adaptive Clustering of Robust Semantic Representations for Adversarial Image Purification](https://mlsanity.com/paper/2104.02155/) (2021)
- [Dual Head Adversarial Training](https://mlsanity.com/paper/2104.10377/) (2021)
- [Robustness-via-Synthesis: Robust Training with Generative Adversarial Perturbations](https://mlsanity.com/paper/2108.09713/) (2021)
- [RAF: Recursive Adversarial Attacks on Face Recognition Using Extremely Limited Queries](https://mlsanity.com/paper/2207.01149/) (2022)
- [Is Face Recognition Safe from Realizable Attacks?](https://mlsanity.com/paper/2210.08178/) (2022)
- [Tightening Robustness Verification of MaxPool-based Neural Networks via Minimizing the Over-Approximation Zone](https://mlsanity.com/paper/2211.09810/) (2022)
- [Do we need entire training data for adversarial training?](https://mlsanity.com/paper/2303.06241/) (2023)
- [IAP: Invisible Adversarial Patch Attack through Perceptibility-Aware Localization and Perturbation Optimization](https://mlsanity.com/paper/2507.06856/) (2025)
- [Trans-defense: Transformer-based Denoiser for Adversarial Defense with Spatial-Frequency Domain Representation](https://mlsanity.com/paper/2510.27245/) (2025)
- [Examining the Human Perceptibility of Black-Box Adversarial Attacks on Face Recognition](https://mlsanity.com/paper/2107.09126/) (2021)
- [Repairing Adversarial Texts through Perturbation](https://mlsanity.com/paper/2201.02504/) (2021)
- [Towards Assessing and Characterizing the Semantic Robustness of Face Recognition](https://mlsanity.com/paper/2202.04978/) (2022)
- [Attack Analysis of Face Recognition Authentication Systems Using Fast Gradient Sign Method](https://mlsanity.com/paper/2203.05653/) (2022)
- [Robust Smart Home Face Recognition under Starving Federated Data](https://mlsanity.com/paper/2211.05410/) (2022)
- [Simultaneous Adversarial Attacks On Multiple Face Recognition System Components](https://mlsanity.com/paper/2304.05048/) (2023)
- [Towards General Robustness Verification of MaxPool-based Convolutional Neural Networks via Tightening Linear Approximation](https://mlsanity.com/paper/2406.00699/) (2024)
- [Discerning the Chaos: Detecting Adversarial Perturbations while Disentangling Intentional from Unintentional Noises](https://mlsanity.com/paper/2409.19619/) (2024)
- [SAP-DIFF: Semantic Adversarial Patch Generation for Black-Box Face Recognition Models via Diffusion Models](https://mlsanity.com/paper/2502.19710/) (2025)
- [Robustness in AI-Generated Detection: Enhancing Resistance to Adversarial Attacks](https://mlsanity.com/paper/2505.03435/) (2025)

## Abstract (arXiv)

Deep neural network (DNN) architecture based models have high expressive power and learning capacity. However, they are essentially a black box method since it is not easy to mathematically formulate the functions that are learned within its many layers of representation. Realizing this, many researchers have started to design methods to exploit the drawbacks of deep learning based algorithms questioning their robustness and exposing their singularities. In this paper, we attempt to unravel three aspects related to the robustness of DNNs for face recognition: (i) assessing the impact of deep architectures for face recognition in terms of vulnerabilities to attacks inspired by commonly observed distortions in the real world that are well handled by shallow learning methods along with learning based adversaries; (ii) detecting the singularities by characterizing abnormal filter response behavior in the hidden layers of deep networks; and (iii) making corrections to the processing pipeline to alleviate the problem. Our experimental evaluation using multiple open-source DNN-based face recognition networks, including OpenFace and VGG-Face, and two publicly available databases (MEDS and PaSC) demonstrates that the performance of deep learning based face recognition algorithms can suffer greatly in the presence of such distortions. The proposed method is also compared with existing detection algorithms and the results show that it is able to detect the attacks with very high accuracy by suitably designing a classifier using the response of the hidden layers in the network. Finally, we present several effective countermeasures to mitigate the impact of adversarial attacks and improve the overall robustness of DNN-based face recognition.