Use a real-life example of an AI system to discuss some impacts of cyber attacks : Practical Backdoor Attacks

The video tutorial explains backdoor defenses in neural networks, focusing on identifying and reconstructing backdoor triggers. It covers the concept of backdoor attacks, where hidden patterns cause unexpected behavior when specific triggers are added. The tutorial demonstrates how to detect backdoors by measuring perturbations and running detection code on a GPU. It also explains using outlier detection to identify significant triggers, highlighting the importance of understanding and mitigating backdoor threats in AI models.

What is a backdoor in a deep neural network?

• A method to improve model accuracy

• A way to reduce model size

• A technique to speed up model training

• A hidden pattern that causes unexpected behavior with a specific trigger

How is a backdoor created in a neural network?

• By using a special trigger during training

• By increasing the number of layers

• By using a different activation function

• By reducing the dataset size

What is the first step in detecting backdoors in a model?

• Reducing the model's parameters

• Changing the model's architecture

• Increasing the model's complexity

• Finding the minimal trigger for misclassification

Which technique is used to detect outliers in the backdoor detection process?

• Median Absolute Deviation (MAD)

• Principal Component Analysis (PCA)

• K-means Clustering

• Linear Regression

What indicates a significant outlier in the context of backdoor detection?

• A low loss value

• A large dataset size

• An anomaly index larger than two

• A high accuracy score