A Survey on Artificial Intelligence Systems Robustness: Adversarial Attacks and Defenses
Abstract
Artificial intelligence systems have achieved widespread applications across many fields such as image classification, speech recognition, and game playing. However, as their decision-making logic is primarily learned from data, their outputs are highly sensitive to data anomalies and are particularly vulnerable to adversarial perturbations. This paper conducts a comprehensive survey on the robustness of artificial intelligence systems, reviewing classical adversarial attack and defense methods, and summarizing future development trends. We hope this work can provide valuable insights for research on the robustness of artificial intelligence systems and support the development of trustworthy artificial intelligence.
Fields of Study & Subjects
Adversarial Robustness in Machine Learning
Security and Verification in Computing
Digital Media Forensic Detection
Publication details
- Journal / source
- Journal of Electronic Research and Application
- Volume
- 10
- Issue
- 1
- Pages
- 182–191
- Publication date
- 27 February 2026
- Language
- EN