Adversarial Attacks and Defenses in Images, Graphs and Text: A Review

Han Xu, Yao Ma, Hao Chen Liu, Debayan Deb, Hui Liu, Ji Liang Tang, Anil K. Jain

Research output: Contribution to journalReview articlepeer-review

352 Scopus citations


Deep neural networks (DNN) have achieved unprecedented success in numerous machine learning tasks in various domains. However, the existence of adversarial examples raises our concerns in adopting deep learning to safety-critical applications. As a result, we have witnessed increasing interests in studying attack and defense mechanisms for DNN models on different data types, such as images, graphs and text. Thus, it is necessary to provide a systematic and comprehensive overview of the main threats of attacks and the success of corresponding countermeasures. In this survey, we review the state of the art algorithms for generating adversarial examples and the countermeasures against adversarial examples, for three most popular data types, including images, graphs and text.

Original languageEnglish (US)
Pages (from-to)151-178
Number of pages28
JournalInternational Journal of Automation and Computing
Issue number2
StatePublished - Apr 1 2020
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Modeling and Simulation
  • Computer Science Applications
  • Applied Mathematics


  • Adversarial example
  • deep learning
  • defenses
  • model safety
  • robustness


Dive into the research topics of 'Adversarial Attacks and Defenses in Images, Graphs and Text: A Review'. Together they form a unique fingerprint.

Cite this