Adversarial Machine Learning: Theories And Applications

Adversarial Machine Learning is a rapidly growing research area at the intersection of machine learning, cybersecurity, and artificial intelligence. It deals with the study of vulnerabilities and defenses of machine learning models against adversarial attacks. This multidisciplinary topic aims to explore the recent advancements and applications of Adversarial Machine Learning, including computer vision, natural language processing, and more. We invite researchers to submit original works that shed light on the theories and practical applications of Adversarial Machine Learning and the development of intelligent defense techniques to safeguard the integrity and reliability of machine learning models in real-world applications.