Defense Model

Defense models for machine learning systems aim to protect against various attacks, including adversarial examples, data poisoning, and model stealing. Current research focuses on developing robust defenses using techniques like reinforcement learning, information-theoretic approaches, and adversarial training, often incorporating deep neural networks. The effectiveness of these defenses is critically evaluated, with a growing emphasis on rigorous testing and identifying vulnerabilities to ensure reliable performance in high-stakes applications such as national security and healthcare. This field is crucial for building trustworthy AI systems capable of withstanding malicious manipulation.