Unsupervised one-point attack based on differential evolution

Yan Yu; Yiting Cheng; Yuyao Ge

doi:10.1117/12.3005108

9 October 2023 Unsupervised one-point attack based on differential evolution

Yan Yu, Yiting Cheng, Yuyao Ge

Proceedings Volume 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023); 1279113 (2023) https://doi.org/10.1117/12.3005108
Event: Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 2023, Qingdao, SD, China

Abstract

With the continuous improvement of artificial intelligence technology and the rapid development of big data applications, deep learning technology has been widely used in various fields. Although deep learning has achieved great success in many fields, it still faces some potential problems. For example, the insecurity of neural networks has raised concerns as this may lead to security vulnerabilities in critical areas. This article proposes an adversarial attack method based on unsupervised algorithms to search for sensitive points in time series dataset and use differential evolution algorithm to perform one-point attack on sensitive points. The experiment was conducted under various experimental parameters. All experimental results are presented in the form of a heatmap, and the article provides a detailed analysis of the experimental results.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yan Yu, Yiting Cheng, and Yuyao Ge "Unsupervised one-point attack based on differential evolution", Proc. SPIE 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 1279113 (9 October 2023); https://doi.org/10.1117/12.3005108

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