# DeepSearch: A Simple and Effective Blackbox Attack for Deep Neural Networks

arXiv: https://arxiv.org/abs/1910.06296 · HTML: https://mlsanity.com/paper/1910.06296/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Oct 2019.

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

- [Machine Translation Testing via Syntactic Tree Pruning](https://mlsanity.com/paper/2401.00751/) (2024)

## Abstract (arXiv)

Although deep neural networks have been very successful in image-classification tasks, they are prone to adversarial attacks. To generate adversarial inputs, there has emerged a wide variety of techniques, such as black- and whitebox attacks for neural networks. In this paper, we present DeepSearch, a novel fuzzing-based, query-efficient, blackbox attack for image classifiers. Despite its simplicity, DeepSearch is shown to be more effective in finding adversarial inputs than state-of-the-art blackbox approaches. DeepSearch is additionally able to generate the most subtle adversarial inputs in comparison to these approaches.