Table of Content

Security, Privacy, and Robustness for Trustworthy AI Systems

Submission Deadline: 31 March 2024 Submit to Special Issue

Guest Editors

Dr. Yongjun Ren, Nanjing University of Information Science and Technology, China.
Dr. Weizhi Meng, Technical University of Denmark, Denmark.
Dr. Chunhua Su, University of Aizu, Japan.
Dr. Chao Chen, RMIT University, Australia.


As artificial intelligence (AI) technology continues to penetrate and be applied in social, economic, and life fields, researchers have become increasingly concerned about the security issues of AI. Despite its immense potential, AI technology, particularly deep learning, is plagued by problems such as robustness, model backdoor, fairness, and privacy. Given the high complexity and difficulty in interpreting neural network models, detecting and defending against these security risks remains a significant challenge. This is particularly critical in safety-related fields such as aerospace, intelligent medicine, and unmanned aerial vehicles, where the credibility, reliability, and interpretability of AI are of utmost importance. Thus, ensuring the safety of AI has become a crucial trend and hotspot of research both domestically and abroad.


This special issue aims to bring together the latest security research on Security, Privacy, and Robustness techniques for trustworthy AI systems. We also welcome the authors to introduce other recent advances addressing the above issues.


Potential topics include but are not limited to:

  • Attack and defense technology of AI systems

  • Explainable AI and interpretability

  • Fairness, bias, and discrimination in AI systems

  • Privacy and data protection in AI systems

  • Security and privacy in federated learning

  • Robustness in federated learning model

  • Automated verification and testing of AI systems

  • Fuzzy testing technology for AI Systems

  • Privacy risk assessment technology for AI systems

  • Application of AI in software engineering and information security


artificial intelligence, federated learning, robustness, security, privacy

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