# Adversarial Laser Beam: Effective Physical-World Attack to DNNs in a Blink

arXiv: https://arxiv.org/abs/2103.06504 · HTML: https://mlsanity.com/paper/2103.06504/

## Key facts

- Cited by 75 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 30 references that are in the map.
- First posted on arXiv 11 Mar 2021.

## Builds on (most cited first, 22 of 22)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [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)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Adversarial examples in the physical world](https://mlsanity.com/paper/1607.02533/) (2016)
- [Improving Transferability of Adversarial Examples with Input Diversity](https://mlsanity.com/paper/1803.06978/) (2018)
- [Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods](https://mlsanity.com/paper/1705.07263/) (2017)
- [Adversarial Patch](https://mlsanity.com/paper/1712.09665/) (2017)
- [Simple Black-box Adversarial Attacks](https://mlsanity.com/paper/1905.07121/) (2019)
- [EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples](https://mlsanity.com/paper/1709.04114/) (2017)
- [Adversarial Camouflage: Hiding Physical-World Attacks with Natural Styles](https://mlsanity.com/paper/2003.08757/) (2020)
- [Universal Physical Camouflage Attacks on Object Detectors](https://mlsanity.com/paper/1909.04326/) (2019)
- [ColorFool: Semantic Adversarial Colorization](https://mlsanity.com/paper/1911.10891/) (2019)
- [Semantic Adversarial Examples](https://mlsanity.com/paper/1804.00499/) (2018)
- [Towards Large yet Imperceptible Adversarial Image Perturbations with Perceptual Color Distance](https://mlsanity.com/paper/1911.02466/) (2019)
- [Robust Physical-World Attacks on Deep Learning Models](https://mlsanity.com/paper/1707.08945/) (2017)
- [Adversarial Attacks Beyond the Image Space](https://mlsanity.com/paper/1711.07183/) (2017)
- [Adversarial Light Projection Attacks on Face Recognition Systems: A Feasibility Study](https://mlsanity.com/paper/2003.11145/) (2020)
- [Invisible Mask: Practical Attacks on Face Recognition with Infrared](https://mlsanity.com/paper/1803.04683/) (2018)
- [Physical Adversarial Textures that Fool Visual Object Tracking](https://mlsanity.com/paper/1904.11042/) (2019)

## Built on by (most cited first, 30 of 75)

- [Advances in adversarial attacks and defenses in computer vision: A survey](https://mlsanity.com/paper/2108.00401/) (2021)
- [Shadows can be Dangerous: Stealthy and Effective Physical-world Adversarial Attack by Natural Phenomenon](https://mlsanity.com/paper/2203.03818/) (2022)
- [Physical Adversarial Attack meets Computer Vision: A Decade Survey](https://mlsanity.com/paper/2209.15179/) (2022)
- [AdvDrop: Adversarial Attack to DNNs by Dropping Information](https://mlsanity.com/paper/2108.09034/) (2021)
- [Optical Adversarial Attack](https://mlsanity.com/paper/2108.06247/) (2021)
- [Beyond ImageNet Attack: Towards Crafting Adversarial Examples for Black-box Domains](https://mlsanity.com/paper/2201.11528/) (2022)
- [Infrared Invisible Clothing:Hiding from Infrared Detectors at Multiple Angles in Real World](https://mlsanity.com/paper/2205.05909/) (2022)
- [Prior-Guided Adversarial Initialization for Fast Adversarial Training](https://mlsanity.com/paper/2207.08859/) (2022)
- [Towards Robust and Secure Embodied AI: A Survey on Vulnerabilities and Attacks](https://mlsanity.com/paper/2502.13175/) (2025)
- [HOTCOLD Block: Fooling Thermal Infrared Detectors with a Novel Wearable Design](https://mlsanity.com/paper/2212.05709/) (2022)
- [A Survey on Physical Adversarial Attack in Computer Vision](https://mlsanity.com/paper/2209.14262/) (2022)
- [Physical-World Optical Adversarial Attacks on 3D Face Recognition](https://mlsanity.com/paper/2205.13412/) (2022)
- [RFLA: A Stealthy Reflected Light Adversarial Attack in the Physical World](https://mlsanity.com/paper/2307.07653/) (2023)
- [Defensive Patches for Robust Recognition in the Physical World](https://mlsanity.com/paper/2204.06213/) (2022)
- [Physical Adversarial Attacks For Camera-based Smart Systems: Current Trends, Categorization, Applications, Research Challenges, and Future Outlook](https://mlsanity.com/paper/2308.06173/) (2023)
- [Adversarial Training of Self-supervised Monocular Depth Estimation against Physical-World Attacks](https://mlsanity.com/paper/2301.13487/) (2023)
- [Scattering Model Guided Adversarial Examples for SAR Target Recognition: Attack and Defense](https://mlsanity.com/paper/2209.04779/) (2022)
- [Invisible Reflections: Leveraging Infrared Laser Reflections to Target Traffic Sign Perception](https://mlsanity.com/paper/2401.03582/) (2024)
- [Preventing Catastrophic Overfitting in Fast Adversarial Training: A Bi-level Optimization Perspective](https://mlsanity.com/paper/2407.12443/) (2024)
- [Breaking Barriers in Physical-World Adversarial Examples: Improving Robustness and Transferability via Robust Feature](https://mlsanity.com/paper/2412.16958/) (2024)
- [RA-BNN: Constructing Robust & Accurate Binary Neural Network to Simultaneously Defend Adversarial Bit-Flip Attack and Improve Accuracy](https://mlsanity.com/paper/2103.13813/) (2021)
- [Adversarial Laser Spot: Robust and Covert Physical-World Attack to DNNs](https://mlsanity.com/paper/2206.01034/) (2022)
- [Adversarial Color Film: Effective Physical-World Attack to DNNs](https://mlsanity.com/paper/2209.02430/) (2022)
- [Visual Adversarial Attacks and Defenses in the Physical World: A Survey](https://mlsanity.com/paper/2211.01671/) (2022)
- [Adversarial Infrared Blocks: A Multi-view Black-box Attack to Thermal Infrared Detectors in Physical World](https://mlsanity.com/paper/2304.10712/) (2023)
- [Adversarial Examples in the Physical World: A Survey](https://mlsanity.com/paper/2311.01473/) (2023)
- [Adversarial Infrared Curves: An Attack on Infrared Pedestrian Detectors in the Physical World](https://mlsanity.com/paper/2312.14217/) (2023)
- [State-of-the-art optical-based physical adversarial attacks for deep learning computer vision systems](https://mlsanity.com/paper/2303.12249/) (2023)
- [Fast Propagation is Better: Accelerating Single-Step Adversarial Training via Sampling Subnetworks](https://mlsanity.com/paper/2310.15444/) (2023)
- [Machine Learning Robustness: A Primer](https://mlsanity.com/paper/2404.00897/) (2024)

## Abstract (arXiv)

Though it is well known that the performance of deep neural networks (DNNs) degrades under certain light conditions, there exists no study on the threats of light beams emitted from some physical source as adversarial attacker on DNNs in a real-world scenario. In this work, we show by simply using a laser beam that DNNs are easily fooled. To this end, we propose a novel attack method called Adversarial Laser Beam (AdvLB), which enables manipulation of laser beam's physical parameters to perform adversarial attack. Experiments demonstrate the effectiveness of our proposed approach in both digital- and physical-settings. We further empirically analyze the evaluation results and reveal that the proposed laser beam attack may lead to some interesting prediction errors of the state-of-the-art DNNs. We envisage that the proposed AdvLB method enriches the current family of adversarial attacks and builds the foundation for future robustness studies for light.