ADSAttack: An Adversarial Attack Algorithm via Searching Adversarial Distribution in Latent Space

Haobo Wang, Chenxi Zhu, Yangjie Cao (Corresponding Author), Yan Zhuang, Jie Li, Xianfu Chen

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)


Deep neural networks are susceptible to interference from deliberately crafted noise, which can lead to incorrect classification results. Existing approaches make less use of latent space information and conduct pixel-domain modification in the input space instead, which increases the computational cost and decreases the transferability. In this work, we propose an effective adversarial distribution searching-driven attack (ADSAttack) algorithm to generate adversarial examples against deep neural networks. ADSAttack introduces an affiliated network to search for potential distributions in image latent space for synthesizing adversarial examples. ADSAttack uses an edge-detection algorithm to locate low-level feature mapping in input space to sketch the minimum effective disturbed area. Experimental results demonstrate that ADSAttack achieves higher transferability, better imperceptible visualization, and faster generation speed compared to traditional algorithms. To generate 1000 adversarial examples, ADSAttack takes (Formula presented.) and, on average, achieves a success rate of (Formula presented.).

Original languageEnglish
Article number816
Issue number4
Publication statusPublished - Feb 2023
MoE publication typeA1 Journal article-refereed


  • adversarial attack
  • adversarial distribution searching
  • edge-detection algorithm
  • latent space


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