# Towards exploring adversarial learning for anomaly detection in complex driving scenes

arXiv: https://arxiv.org/abs/2307.05256 · HTML: https://mlsanity.com/paper/2307.05256/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 24 references that are in the map.
- First posted on arXiv 17 Jun 2023.

## Builds on (most cited first, 6 of 6)

- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning](https://mlsanity.com/paper/1805.04687/) (2018)
- [Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery](https://mlsanity.com/paper/1703.05921/) (2017)
- [GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training](https://mlsanity.com/paper/1805.06725/) (2018)
- [Adversarially Learned One-Class Classifier for Novelty Detection](https://mlsanity.com/paper/1802.09088/) (2018)
- [Autoencoder-based Semantic Novelty Detection: Towards Dependable AI-based Systems](https://mlsanity.com/paper/2108.10851/) (2021)

## Built on by (most cited first, 2 of 2)

- [Deciphering the Definition of Adversarial Robustness for post-hoc OOD Detectors](https://mlsanity.com/paper/2406.15104/) (2024)
- [A Method for the Runtime Validation of AI-based Environment Perception in Automated Driving System](https://mlsanity.com/paper/2412.16762/) (2024)

## Abstract (arXiv)

One of the many Autonomous Systems (ASs), such as autonomous driving cars, performs various safety-critical functions. Many of these autonomous systems take advantage of Artificial Intelligence (AI) techniques to perceive their environment. But these perceiving components could not be formally verified, since, the accuracy of such AI-based components has a high dependency on the quality of training data. So Machine learning (ML) based anomaly detection, a technique to identify data that does not belong to the training data could be used as a safety measuring indicator during the development and operational time of such AI-based components. Adversarial learning, a sub-field of machine learning has proven its ability to detect anomalies in images and videos with impressive results on simple data sets. Therefore, in this work, we investigate and provide insight into the performance of such techniques on a highly complex driving scenes dataset called Berkeley DeepDrive.