Deep neural networks can be tricked by fake data called adversarial examples. Researchers are working on making these networks more resistant to such attacks. By using robust optimization, they found ways to train networks that are better at defending against different types of attacks. These methods provide a concrete security guarantee that can protect against any adversary. This approach helps in creating deep learning models that are more secure and reliable.