# Adaptive Adversarial Attack on Scene Text Recognition

arXiv: https://arxiv.org/abs/1807.03326 · HTML: https://mlsanity.com/paper/1807.03326/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2018.

## Built on by (most cited first, 2 of 2)

- [Adversarial Robustness of Visual Dialog](https://mlsanity.com/paper/2207.02639/) (2022)
- [STR-Cert: Robustness Certification for Deep Text Recognition on Deep Learning Pipelines and Vision Transformers](https://mlsanity.com/paper/2401.05338/) (2023)

## Abstract (arXiv)

Recent studies have shown that state-of-the-art deep learning models are vulnerable to the inputs with small perturbations (adversarial examples). We observe two critical obstacles in adversarial examples: (i) Strong adversarial attacks (e.g., C&W attack) require manually tuning hyper-parameters and take a long time to construct an adversarial example, making it impractical to attack real-time systems; (ii) Most of the studies focus on non-sequential tasks, such as image classification, yet only a few consider sequential tasks. In this work, we speed up adversarial attacks, especially on sequential learning tasks. By leveraging the uncertainty of each task, we directly learn the adaptive multi-task weightings, without manually searching hyper-parameters. A unified architecture is developed and evaluated for both non-sequential tasks and sequential ones. To validate the effectiveness, we take the scene text recognition task as a case study. To our best knowledge, our proposed method is the first attempt to adversarial attack for scene text recognition. Adaptive Attack achieves over 99.9% success rate with 3-6X speedup compared to state-of-the-art adversarial attacks.