# Robust SleepNets

arXiv: https://arxiv.org/abs/2102.12555 · HTML: https://mlsanity.com/paper/2102.12555/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 32 references that are in the map.
- First posted on arXiv 24 Feb 2021.

## Builds on (most cited first, 12 of 12)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [DeepFool: a simple and accurate method to fool deep neural networks](https://mlsanity.com/paper/1511.04599/) (2015)
- [On Adaptive Attacks to Adversarial Example Defenses](https://mlsanity.com/paper/2002.08347/) (2020)
- [Adversarial Logit Pairing](https://mlsanity.com/paper/1803.06373/) (2018)
- [Deep Sparse Coding for Invariant Multimodal Halle Berry Neurons](https://mlsanity.com/paper/1711.07998/) (2017)
- [Adversarial Attacks on Convolutional Neural Networks in Facial Recognition Domain](https://mlsanity.com/paper/2001.11137/) (2020)
- [Machine-learned metrics for predicting the likelihood of success in materials discovery](https://mlsanity.com/paper/1911.11201/) (2019)
- [Perfecting the Crime Machine](https://mlsanity.com/paper/2001.09764/) (2020)
- [Adversarial Attacks against Neural Networks in Audio Domain: Exploiting Principal Components](https://mlsanity.com/paper/2007.07001/) (2020)
- [Towards Evaluating Driver Fatigue with Robust Deep Learning Models](https://mlsanity.com/paper/2007.08453/) (2020)

## Built on by (most cited first, 1 of 1)

- [ATRAS: Adversarially Trained Robust Architecture Search](https://mlsanity.com/paper/2106.06917/) (2021)

## Abstract (arXiv)

State-of-the-art convolutional neural networks excel in machine learning tasks such as face recognition, and object classification but suffer significantly when adversarial attacks are present. It is crucial that machine critical systems, where machine learning models are deployed, utilize robust models to handle a wide range of variability in the real world and malicious actors that may use adversarial attacks. In this study, we investigate eye closedness detection to prevent vehicle accidents related to driver disengagements and driver drowsiness. Specifically, we focus on adversarial attacks in this application domain, but emphasize that the methodology can be applied to many other domains. We develop two models to detect eye closedness: first model on eye images and a second model on face images. We adversarially attack the models with Projected Gradient Descent, Fast Gradient Sign and DeepFool methods and report adversarial success rate. We also study the effect of training data augmentation. Finally, we adversarially train the same models on perturbed images and report the success rate for the defense against these attacks. We hope our study sets up the work to prevent potential vehicle accidents by capturing drivers' face images and alerting them in case driver's eyes are closed due to drowsiness.