The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
DOI: 10.4121/1753c8aa-adf4-4256-ae74-02e34ba120cf

Qiao, Yanqi; Liu, Dazhuang (2025): Code for the publication "Low-Frequency Black-Box Backdoor Attack via Evolutionary Algorithm". Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/1753c8aa-adf4-4256-ae74-02e34ba120cf.v1

Categories

• Artificial Intelligence and Image Processing
• Information and Computing Sciences

Keywords

backdoor attack black-box computer vision frequency domain robustness simulated annealing

Licence

MIT

Interoperability

• RO-Crate Metadata

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by Yanqi Qiao , Dazhuang Liu

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.

History

• 2025-05-19 first online, published, posted

Publisher

4TU.ResearchData

Format

script/.py

Associated peer-reviewed publication

Low-Frequency Black-Box Backdoor Attack via Evolutionary Algorithm

Organizations

TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science, Cybersecurity