Evaluate the impact of privacy issues, cyberattacks, and malware on your AI application : The Current State of Defenses

The video tutorial explores various defense theories, focusing on three main approaches: modified training, modified input, and adone detection. Each approach is discussed in terms of its advantages, disadvantages, and specific applications. Modified training involves methods like adversarial training and regularization but can be time-consuming. Modified input methods, such as reconstruction and encoding, are application-specific. Adone detection uses classifiers to identify outliers and unknown categories, offering diverse quality and speed. The video emphasizes the need to test multiple options as there is no one-size-fits-all solution.

What is the significance of the statement 'there is no silver bullet' in the context of defense approaches?

Why is it important to test multiple options against your solution in defense theories?