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.

Which of the following is a disadvantage of the modified training approach?

• It is not suitable for face recognition.

• It is not widely researched.

• It is very time-consuming.

• It lacks practical examples.

What is a characteristic of the modified input approach?

• It requires no prior research.

• It is faster than other methods.

• It is application-specific.

• It is universally applicable.

Which method is an example of adone detection?

• Regularization

• Adversarial training

• Binary classifiers

• Distillation

What is a common feature of adone detection methods?

• They are only used in face recognition.

• They have uniform quality.

• They are all very fast.

• They are diverse in quality and speed.

What is the key takeaway regarding the use of different defense approaches?

• One approach is always sufficient.

• Multiple options should be tested.

• Adone detection is the best solution.

• Only modified input is effective.