# LaserGuider: A Laser Based Physical Backdoor Attack against Deep Neural Networks

arXiv: https://arxiv.org/abs/2412.03993 · HTML: https://mlsanity.com/paper/2412.03993/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 56 references that are in the map.
- First posted on arXiv 5 Dec 2024.

## Builds on (most cited first, 9 of 9)

- [BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain](https://mlsanity.com/paper/1708.06733/) (2017)
- [Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning](https://mlsanity.com/paper/1712.05526/) (2017)
- [On Evaluating Adversarial Robustness](https://mlsanity.com/paper/1902.06705/) (2019)
- [Label-Consistent Backdoor Attacks](https://mlsanity.com/paper/1912.02771/) (2019)
- [Backdoor Attack in the Physical World](https://mlsanity.com/paper/2104.02361/) (2021)
- [Autonomous Driving with Deep Learning: A Survey of State-of-Art Technologies](https://mlsanity.com/paper/2006.06091/) (2020)
- [Light Can Hack Your Face! Black-box Backdoor Attack on Face Recognition Systems](https://mlsanity.com/paper/2009.06996/) (2020)
- [DiffPhysBA: Diffusion-based Physical Backdoor Attack against Person Re-Identification in Real-World](https://mlsanity.com/paper/2405.19990/) (2024)
- [Invisible Threats: Backdoor Attack in OCR Systems](https://mlsanity.com/paper/2310.08259/) (2023)

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

- [FIGhost: Fluorescent Ink-based Stealthy and Flexible Backdoor Attacks on Physical Traffic Sign Recognition](https://mlsanity.com/paper/2505.12045/) (2025)

## Abstract (arXiv)

Backdoor attacks embed hidden associations between triggers and targets in deep neural networks (DNNs), causing them to predict the target when a trigger is present while maintaining normal behavior otherwise. Physical backdoor attacks, which use physical objects as triggers, are feasible but lack remote control, temporal stealthiness, flexibility, and mobility. To overcome these limitations, in this work, we propose a new type of backdoor triggers utilizing lasers that feature long-distance transmission and instant-imaging properties. Based on the laser-based backdoor triggers, we present a physical backdoor attack, called LaserGuider, which possesses remote control ability and achieves high temporal stealthiness, flexibility, and mobility. We also introduce a systematic approach to optimize laser parameters for improving attack effectiveness. Our evaluation on traffic sign recognition DNNs, critical in autonomous vehicles, demonstrates that LaserGuider with three different laser-based triggers achieves over 90% attack success rate with negligible impact on normal inputs. Additionally, we release LaserMark, the first dataset of real world traffic signs stamped with physical laser spots, to support further research in backdoor attacks and defenses.