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

Source: [DeepSearch: A Simple and Effective Blackbox Attack for Deep Neural Networks](<https://www.sota2.com/research/paper/68fa68261e209131cea1a45c>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T06:03:45.482Z

Authors: Fuyuan Zhang, Sankalan Pal Chowdhury, Maria Christakis

Published: 2019-10-14T17:25:21.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1910.06296>)

[PDF](<https://arxiv.org/pdf/1910.06296.pdf>)