Deep learning systems can be tricked by malicious inputs, leading to serious security risks. A method called defensive distillation can help protect these systems by making it much harder to create harmful inputs. The use of defensive distillation can reduce the effectiveness of these harmful inputs by a huge amount, making them almost ineffective. This defense mechanism works by weakening the signals that guide the creation of harmful inputs, making it much harder to manipulate the system.