# Adversarial Attack Against Image-Based Localization Neural Networks

arXiv: https://arxiv.org/abs/2210.06589 · HTML: https://mlsanity.com/paper/2210.06589/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 24 references that are in the map.
- First posted on arXiv 11 Oct 2022.

## Builds on (most cited first, 13 of 13)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://mlsanity.com/paper/1812.05784/) (2018)
- [NetVLAD: CNN architecture for weakly supervised place recognition](https://mlsanity.com/paper/1511.07247/) (2015)
- [Adversarial Patch](https://mlsanity.com/paper/1712.09665/) (2017)
- [Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models](https://mlsanity.com/paper/1712.04248/) (2017)
- [A Survey of Autonomous Driving: Common Practices and Emerging Technologies](https://mlsanity.com/paper/1906.05113/) (2019)
- [Improving Semantic Segmentation via Video Propagation and Label Relaxation](https://mlsanity.com/paper/1812.01593/) (2018)
- [Lidar for Autonomous Driving: The principles, challenges, and trends for automotive lidar and perception systems](https://mlsanity.com/paper/2004.08467/) (2020)
- [SegStereo: Exploiting Semantic Information for Disparity Estimation](https://mlsanity.com/paper/1807.11699/) (2018)
- [PhysGAN: Generating Physical-World-Resilient Adversarial Examples for Autonomous Driving](https://mlsanity.com/paper/1907.04449/) (2019)
- [Unadversarial Examples: Designing Objects for Robust Vision](https://mlsanity.com/paper/2012.12235/) (2020)
- [Multi-Object Tracking using Poisson Multi-Bernoulli Mixture Filtering for Autonomous Vehicles](https://mlsanity.com/paper/2103.07783/) (2021)

## Abstract (arXiv)

In this paper, we present a proof of concept for adversarially attacking the image-based localization module of an autonomous vehicle. This attack aims to cause the vehicle to perform a wrong navigational decisions and prevent it from reaching a desired predefined destination in a simulated urban environment. A database of rendered images allowed us to train a deep neural network that performs a localization task and implement, develop and assess the adversarial pattern. Our tests show that using this adversarial attack we can prevent the vehicle from turning at a given intersection. This is done by manipulating the vehicle's navigational module to falsely estimate its current position and thus fail to initialize the turning procedure until the vehicle misses the last opportunity to perform a safe turn in a given intersection.