cff-version: 1.2.0
abstract: "<p>This research aims to investigate the vulnerabilities of existing convolutional neural networks (CNNs) and vision transformers (ViTs) against backdoor attacks and to develop a novel backdooring approach. The study focuses on advancing a new technology in this area. The research uses textual data, with all data (i.e., source code) being independently developed.</p>"
authors:
  - family-names: Qiao
    given-names: Yanqi
    orcid: "https://orcid.org/0000-0003-0180-0096"
  - family-names: Liu
    given-names: Dazhuang
    orcid: "https://orcid.org/0000-0002-7250-1264"
title: "Code for the publication &#34;Low-Frequency Black-Box Backdoor Attack via Evolutionary Algorithm&#34;"
keywords:
version: 1
identifiers:
  - type: doi
    value: 10.4121/1753c8aa-adf4-4256-ae74-02e34ba120cf.v1
license: MIT
date-released: 2025-05-19