Advances in Secure AI: Technology and Applications

Dear Colleagues,

Artificial intelligence (AI) is a technology that enables us to identify solutions to complex problems using relatively simple learning mechanisms in a data-driven fashion. Due to the recent success and advances in AI techniques such as computer vision and natural language processing, many intelligent services integrate AI, especially in mission-critical applications dealing with complex systems such as autonomous vehicles, environmental monitoring, and cybersecurity. However, we still do not understand the complete characteristics of AI models and learning techniques; therefore, it has become an urgent call for both theoreticians and partitioners to investigate robust, timely, explainable, and trustworthy AI to avoid unforeseen malfunction of AI-based services.

This Special Issue aims to address the latest advances in the techniques and applications of secure AI. Potential topics include but are not limited to the following:

• Adversarial attack and defense techniques;
• AI model stealing attack and defense techniques;
• Data poisoning (AI backdoor/trojan) attack, detection, and defense;
• Explainable AI (XAI) techniques and applications;
• XAI for human-AI cooperation;
• H/W enclaves for secure AI;
• AI model verification;
• Data privacy;
• Statistical bias and fairness;
• Learning provenance of data;
• Learning causality;
• Robust training;
• Robust decisions in dynamic environments;
• Perspectives of secure AI in real-world applications;
• Sharing confidential data: confidential federated learning, differential privacy, and homomorphic encryption.

Dr. Sangkyun Lee
Prof. Dr. Yunheung Paek
Guest Editors

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