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Questions and Answers
What is a characteristic of machine learning models that allows adversarial examples to be effective against multiple models?
What is a characteristic of machine learning models that allows adversarial examples to be effective against multiple models?
What type of attack involves extracting a model and then generating adversarial examples?
What type of attack involves extracting a model and then generating adversarial examples?
What is the goal of generating adversarial examples?
What is the goal of generating adversarial examples?
Why are defenses against adversarial examples difficult to discuss?
Why are defenses against adversarial examples difficult to discuss?
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What is the term for the ability of adversarial examples to be effective against multiple models?
What is the term for the ability of adversarial examples to be effective against multiple models?
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What type of attack involves manipulating the training data to affect the model's performance?
What type of attack involves manipulating the training data to affect the model's performance?
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What is the term for the process of extracting a model to generate adversarial examples?
What is the term for the process of extracting a model to generate adversarial examples?
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What is the goal of generating adversarial examples for a model?
What is the goal of generating adversarial examples for a model?
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What is a motivation behind model extraction attacks?
What is a motivation behind model extraction attacks?
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What is a characteristic of equation-solving model extraction attacks?
What is a characteristic of equation-solving model extraction attacks?
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What is a category of model extraction attacks besides equation-solving and path finding attacks?
What is a category of model extraction attacks besides equation-solving and path finding attacks?
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What is the goal of model extraction attacks?
What is the goal of model extraction attacks?
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What is the assumption behind path finding attacks?
What is the assumption behind path finding attacks?
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What is a benefit of replicating a machine learning model using model extraction attacks?
What is a benefit of replicating a machine learning model using model extraction attacks?
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What is a characteristic of MLaaS services?
What is a characteristic of MLaaS services?
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What is a motivation behind the popularity of cloud computing?
What is a motivation behind the popularity of cloud computing?
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What is the primary goal of an attacker using adversarial examples?
What is the primary goal of an attacker using adversarial examples?
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How can an attacker rebuild the model using membership queries?
How can an attacker rebuild the model using membership queries?
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What is the property that enables an attacker to automate the process of crafting adversarial examples?
What is the property that enables an attacker to automate the process of crafting adversarial examples?
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What is the purpose of model extraction attacks?
What is the purpose of model extraction attacks?
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What is the goal of an attacker using data poisoning attacks?
What is the goal of an attacker using data poisoning attacks?
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How can an attacker craft adversarial examples using the gradient?
How can an attacker craft adversarial examples using the gradient?
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What is the primary assumption of machine learning algorithms?
What is the primary assumption of machine learning algorithms?
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What is the purpose of membership queries?
What is the purpose of membership queries?
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Study Notes
Model Extraction Attacks
- Model extraction attacks are becoming more prominent due to the popularity of cloud computing and machine learning as a service (MLaaS).
- These attacks involve building a machine that produces the same results as the target model, allowing the attacker to bypass monetization and use the duplicate model offline.
- There are three main categories of model extraction attacks: equation-solving, path finding, and membership queries.
Equation-Solving Model Extraction Attacks
- This type of attack involves tailoring input data to build a linear system of variables and solving it for unknown weights and bias.
- Simple models like logistic regression can be easily recreated with 100% accuracy using this method.
- Even complex models like neural networks can be recreated, although it may be more difficult.
Path Finding Attacks
- This method assumes that each leaf in the decision tree has a unique distribution, allowing the attacker to rebuild the tree by querying the model and tracking which leaf the data falls into.
- By changing the input data one feature at a time, the attacker can figure out the different branches of the tree.
Membership Queries Attacks
- This type of attack involves training a local model, querying the target model, and retraining the local model to adapt to the target model's responses.
- The attacker can assume a model, train it, and then query the points where the local confidence is low.
Adversarial Examples
- Adversarial examples are inputs tailored to cause machine learning models to make mistakes.
- These examples can be crafted using model extraction attacks and the property of transferability.
- Transferability means that an adversarial example for a model in a specific domain will likely be adversarial to any other model trained in that domain.
- Adversarial examples can be used to cause misclassification in machine learning models.
Crafting Adversarial Examples
- Adversarial examples can be crafted by adding a perturbation to the input data based on the gradient and changing the image to a misclassified image with the lowest cost.
- This process can be automated using model extraction attacks and transferability.
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Description
This quiz covers the types of model extraction attacks, including equation-solving attacks, and their relevance to machine learning as a service (MLaaS) and cloud computing.
