# UnMask: Adversarial Detection and Defense Through Robust Feature Alignment

arXiv: https://arxiv.org/abs/2002.09576 · HTML: https://mlsanity.com/paper/2002.09576/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Feb 2020.

## Built on by (most cited first, 8 of 8)

- [Adversarial Example Detection for DNN Models: A Review and Experimental Comparison](https://mlsanity.com/paper/2105.00203/) (2021)
- [Part-Based Models Improve Adversarial Robustness](https://mlsanity.com/paper/2209.09117/) (2022)
- [A.I. Robustness: a Human-Centered Perspective on Technological Challenges and Opportunities](https://mlsanity.com/paper/2210.08906/) (2022)
- [Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A Contemporary Survey](https://mlsanity.com/paper/2303.06302/) (2023)
- [Adversarial Feature Alignment: Balancing Robustness and Accuracy in Deep Learning via Adversarial Training](https://mlsanity.com/paper/2402.12187/) (2024)
- [SkeletonVis: Interactive Visualization for Understanding Adversarial Attacks on Human Action Recognition Models](https://mlsanity.com/paper/2101.10586/) (2021)
- [Detecting and Recovering Adversarial Examples from Extracting Non-robust and Highly Predictive Adversarial Perturbations](https://mlsanity.com/paper/2206.15128/) (2022)
- [Robust Physical Adversarial Patches Using Dynamically Optimized Clusters](https://mlsanity.com/paper/2511.18656/) (2025)

## Abstract (arXiv)

Deep learning models are being integrated into a wide range of high-impact, security-critical systems, from self-driving cars to medical diagnosis. However, recent research has demonstrated that many of these deep learning architectures are vulnerable to adversarial attacks--highlighting the vital need for defensive techniques to detect and mitigate these attacks before they occur. To combat these adversarial attacks, we developed UnMask, an adversarial detection and defense framework based on robust feature alignment. The core idea behind UnMask is to protect these models by verifying that an image's predicted class ("bird") contains the expected robust features (e.g., beak, wings, eyes). For example, if an image is classified as "bird", but the extracted features are wheel, saddle and frame, the model may be under attack. UnMask detects such attacks and defends the model by rectifying the misclassification, re-classifying the image based on its robust features. Our extensive evaluation shows that UnMask (1) detects up to 96.75% of attacks, and (2) defends the model by correctly classifying up to 93% of adversarial images produced by the current strongest attack, Projected Gradient Descent, in the gray-box setting. UnMask provides significantly better protection than adversarial training across 8 attack vectors, averaging 31.18% higher accuracy. We open source the code repository and data with this paper: https://github.com/safreita1/unmask.