# An Overview of Laser Injection against Embedded Neural Network Models

arXiv: https://arxiv.org/abs/2105.01403 · HTML: https://mlsanity.com/paper/2105.01403/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 25 references that are in the map.
- First posted on arXiv 4 May 2021.

## Builds on (most cited first, 12 of 12)

- [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)
- [Adversarial Examples Are Not Bugs, They Are Features](https://mlsanity.com/paper/1905.02175/) (2019)
- [The Limitations of Deep Learning in Adversarial Settings](https://mlsanity.com/paper/1511.07528/) (2015)
- [One pixel attack for fooling deep neural networks](https://mlsanity.com/paper/1710.08864/) (2017)
- [Sign-OPT: A Query-Efficient Hard-label Adversarial Attack](https://mlsanity.com/paper/1909.10773/) (2019)
- [Bit-Flip Attack: Crushing Neural Network with Progressive Bit Search](https://mlsanity.com/paper/1903.12269/) (2019)
- [Sparse and Imperceivable Adversarial Attacks](https://mlsanity.com/paper/1909.05040/) (2019)
- [SparseFool: a few pixels make a big difference](https://mlsanity.com/paper/1811.02248/) (2018)
- [Open DNN Box by Power Side-Channel Attack](https://mlsanity.com/paper/1907.10406/) (2019)
- [TensorFI: A Flexible Fault Injection Framework for TensorFlow Applications](https://mlsanity.com/paper/2004.01743/) (2020)

## Built on by (most cited first, 3 of 3)

- [A Practical Introduction to Side-Channel Extraction of Deep Neural Network Parameters](https://mlsanity.com/paper/2211.05590/) (2022)
- [A Closer Look at Evaluating the Bit-Flip Attack Against Deep Neural Networks](https://mlsanity.com/paper/2209.14243/) (2022)
- [Verification of Bit-Flip Attacks against Quantized Neural Networks](https://mlsanity.com/paper/2502.16286/) (2025)

## Abstract (arXiv)

For many IoT domains, Machine Learning and more particularly Deep Learning brings very efficient solutions to handle complex data and perform challenging and mostly critical tasks. However, the deployment of models in a large variety of devices faces several obstacles related to trust and security. The latest is particularly critical since the demonstrations of severe flaws impacting the integrity, confidentiality and accessibility of neural network models. However, the attack surface of such embedded systems cannot be reduced to abstract flaws but must encompass the physical threats related to the implementation of these models within hardware platforms (e.g., 32-bit microcontrollers). Among physical attacks, Fault Injection Analysis (FIA) are known to be very powerful with a large spectrum of attack vectors. Most importantly, highly focused FIA techniques such as laser beam injection enable very accurate evaluation of the vulnerabilities as well as the robustness of embedded systems. Here, we propose to discuss how laser injection with state-of-the-art equipment, combined with theoretical evidences from Adversarial Machine Learning, highlights worrying threats against the integrity of deep learning inference and claims that join efforts from the theoretical AI and Physical Security communities are a urgent need.