Enemy Spotted

"Enemy Spotted" broadly encompasses research on identifying and mitigating adversarial threats across diverse domains, from AI code generation and medical data security to game AI and image classification. Current research focuses on developing robust models and algorithms, including deep learning architectures and contrastive learning methods, to improve detection and defense against these threats, often leveraging techniques like adversarial training and opponent modeling. This work is crucial for enhancing the security and reliability of AI systems and various applications, addressing concerns about data privacy, model vulnerability, and the ethical implications of AI.