Recent Advances in Artificial Intelligence for Security and Security for Artificial Intelligence

Dear Colleagues,

The rapid evolution of artificial intelligence (AI) has profoundly impacted various sectors, including healthcare, remote sensing, smart cities, and more. For example, AI technologies are being increasingly integrated to analyze and process remote images derived from multi-spectral, hyperspectral, and LiDAR systems. However, as the effectiveness of AI systems heavily depends on the availability and utilization of large datasets, which often contain sensitive personal information, the risks of data breaches, unauthorized access, and the misuse of personal data have become more pressing. In addition, AI models themselves are vulnerable to evolving cyber threats, such as adversarial attacks, model inversion, and data poisoning, which further complicate the landscape of data security and privacy protection. Regulatory frameworks like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) emphasize the need for robust data protection strategies, urging AI systems to adopt advanced security and privacy-preserving technologies. This Topic, "Recent Advances in Artificial Intelligence for Security and Security for Artificial Intelligence", seeks to explore innovative solutions at the intersection of AI and security. We invite submissions that examine advanced approaches, methodologies, and applications of AI to improve security, as well as techniques for securing AI systems themselves.

The topics of interest include but are not limited to the following:

• Secure and efficient encryption algorithms powered by AI; 
• AI-enhanced remote sensing image processing and analysis; 
• Adversarial attacks and defenses in remote sensing image classification; 
• Privacy-preserving AI techniques for remote sensing data (e.g., federated learning and differential privacy); 
• AI-based solutions for securing network protocols; 
• Intelligent authentication systems using machine learning; 
• AI-based vulnerability detection in communication networks; 
• Secure data aggregation and sharing protocols in AI; 
• Privacy risks and mitigation strategies in AI-powered applications; 
• Protecting personal data in AI training and inference; 
• AI models’ resilience to adversarial perturbations; 
• AI for building secure cloud platforms and infrastructures.

Dr. Tao Zhang
Dr. Xiangyun Tang
Dr. Jiacheng Wang
Dr. Chuan Zhang
Prof. Dr. Jiqiang Liu
Topic Editors