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NAVAL POSTGRADUATE SCHOOL MONTEREY, CALIFORNIATHESIS FORMING ADVERSARIAL EXAMPLE ATTACKS AGAINST DEEP NEURAL NETWORKS WITH REINFORCEMENT LEARNING by Matthew D. Akers March 2023 Thesis Advisor: Second

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What is forming adversarial example attacks?

Forming adversarial example attacks refers to the process of creating inputs to machine learning models that are intentionally designed to cause the model to make a mistake. These inputs are typically slightly altered versions of legitimate inputs that mislead the model into producing incorrect outputs.

Who is required to file forming adversarial example attacks?

Typically, researchers and developers working in the field of artificial intelligence who are conducting experiments or assessments involving adversarial machine learning may be required to file or report on forming adversarial example attacks.

How to fill out forming adversarial example attacks?

Filling out forms related to forming adversarial example attacks generally involves documenting the methodology, the specific adversarial examples created, the intended application, any testing results, and potential impacts on model performance.

What is the purpose of forming adversarial example attacks?

The purpose of forming adversarial example attacks is to evaluate the robustness and security of machine learning models, helping to identify vulnerabilities that could be exploited in real-world applications.

What information must be reported on forming adversarial example attacks?

Key information that must be reported includes the nature of the adversarial examples, the techniques used to generate them, the data sets involved, model architecture, evaluation results, and any implications for model deployment.

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