Explore the development of deep learning models resistant to adversarial attacks in this 30-minute lecture from the University of Central Florida. Delve into key concepts such as security against attacks, minmax functions, and saddle point problems. Examine robustness strategies, analyze observations, and review experimental results to gain insights into creating more secure AI systems. Conclude with a summary of essential points for building resilient deep learning models in the face of potential adversarial threats.