Robust Defense

Robust defense in machine learning focuses on developing methods to protect models against various adversarial attacks, including backdoors, jailbreaks, data poisoning, and evasion attacks targeting different model architectures like LLMs and CNNs. Current research emphasizes developing defenses that are both effective against diverse attack strategies and efficient, addressing challenges like resource constraints and privacy concerns through techniques such as randomized smoothing, gradient masking, and reinforcement learning-based approaches. These advancements are crucial for ensuring the reliability and trustworthiness of AI systems across diverse applications, from autonomous driving to medical diagnosis.