# Imperceptible CMOS camera dazzle for adversarial attacks on deep neural networks

arXiv: https://arxiv.org/abs/2311.16118 · HTML: https://mlsanity.com/paper/2311.16118/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 27 references that are in the map.
- First posted on arXiv 22 Oct 2023.

## Builds on (most cited first, 10 of 10)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [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)
- [Adversarial Examples: Attacks and Defenses for Deep Learning](https://mlsanity.com/paper/1712.07107/) (2017)
- [The Translucent Patch: A Physical and Universal Attack on Object Detectors](https://mlsanity.com/paper/2012.12528/) (2020)
- [SLAP: Improving Physical Adversarial Examples with Short-Lived Adversarial Perturbations](https://mlsanity.com/paper/2007.04137/) (2020)
- [Invisible Perturbations: Physical Adversarial Examples Exploiting the Rolling Shutter Effect](https://mlsanity.com/paper/2011.13375/) (2020)
- [Invisible Mask: Practical Attacks on Face Recognition with Infrared](https://mlsanity.com/paper/1803.04683/) (2018)
- [They See Me Rollin': Inherent Vulnerability of the Rolling Shutter in CMOS Image Sensors](https://mlsanity.com/paper/2101.10011/) (2021)
- [Light Can Hack Your Face! Black-box Backdoor Attack on Face Recognition Systems](https://mlsanity.com/paper/2009.06996/) (2020)

## Abstract (arXiv)

Despite the outstanding performance of deep neural networks, they are vulnerable to adversarial attacks. While there are many invisible attacks in the digital domain, most physical world adversarial attacks are visible. Here we present an invisible optical adversarial attack that uses a light source to dazzle a CMOS camera with a rolling shutter. We present the photopic conditions required to keep the attacking light source completely invisible while sufficiently jamming the captured image so that a deep neural network applied to it is deceived.