# On the feasibility of attacking Thai LPR systems with adversarial examples

arXiv: https://arxiv.org/abs/2301.05506 · HTML: https://mlsanity.com/paper/2301.05506/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 16 references that are in the map.
- First posted on arXiv 13 Jan 2023.

## Builds on (most cited first, 8 of 8)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [DeepFool: a simple and accurate method to fool deep neural networks](https://mlsanity.com/paper/1511.04599/) (2015)
- [Black-box Adversarial Attacks with Limited Queries and Information](https://mlsanity.com/paper/1804.08598/) (2018)
- [HopSkipJumpAttack: A Query-Efficient Decision-Based Attack](https://mlsanity.com/paper/1904.02144/) (2019)
- [Fooling OCR Systems with Adversarial Text Images](https://mlsanity.com/paper/1802.05385/) (2018)
- [Attacking Optical Character Recognition (OCR) Systems with Adversarial Watermarks](https://mlsanity.com/paper/2002.03095/) (2020)
- [A Black-Box Attack on Optical Character Recognition Systems](https://mlsanity.com/paper/2208.14302/) (2022)

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

- [Street-Legal Physical-World Adversarial Rim for License Plates](https://mlsanity.com/paper/2604.02457/) (2026)

## Abstract (arXiv)

Recent advances in deep neural networks (DNNs) have significantly enhanced the capabilities of optical character recognition (OCR) technology, enabling its adoption to a wide range of real-world applications. Despite this success, DNN-based OCR is shown to be vulnerable to adversarial attacks, in which the adversary can influence the DNN model's prediction by carefully manipulating input to the model. Prior work has demonstrated the security impacts of adversarial attacks on various OCR languages. However, to date, no studies have been conducted and evaluated on an OCR system tailored specifically for the Thai language. To bridge this gap, this work presents a feasibility study of performing adversarial attacks on a specific Thai OCR application -- Thai License Plate Recognition (LPR). Moreover, we propose a new type of adversarial attack based on the semi-targeted scenario and show that this scenario is highly realistic in LPR applications. Our experimental results show the feasibility of our attacks as they can be performed on a commodity computer desktop with over 90% attack success rate.