Privacy Attacks – Stealing Models
With AI systems increasingly ingrained in our daily lives, from personal assistants to healthcare diagnostics, the potential for privacy breaches has escalated dramatically. This chapter delves into the realm of privacy attacks within adversarial AI, a domain where attackers intentionally manipulate AI models to extract sensitive information, including confidential model information. We will look at the attacks and attack scenarios, provide code examples, and discuss mitigations.
The key sections and topics we will cover are as follows:
- Understanding privacy attacks: Introducing the fundamental concepts of privacy attacks in AI, including model extraction, model inversion, and membership inference attacks, this section sets the stage for a deeper exploration of each attack spread over two chapters.
- Stealing models with model extraction attacks: This chapter will cover model extraction attacks. We will dive into specific types...
