1.7 Chapter Summary
Key Takeaways
- Machine learning security is critical for building reliable and trustworthy systems.
- Threats such as adversarial attacks, data poisoning, and privacy breaches highlight the need for robust defences.
- Balancing security with performance remains a key challenge for practitioners.
- Understanding the attack surface and adversarial capabilities is essential for designing effective security measures.
OpenAI. (2025). ChatGPT. [Large language model]. https://chat.openai.com/chat
Prompt: Can you generate key takeaways for this chapter content?
Key Terms
- Availability maintains uninterrupted access to ML systems and services.
- Black box attack is an adversarial attack for which the adversary has zero knowledge of the victim that is put under attack.
- Confidentiality prevents unauthorized access to sensitive data and models.
- Evasion Attack attacks the machine learning model at test time.
- Gray Box Attack is when an adversary has partial knowledge of the target system.
- Integrity: Ensure that ML models and data remain unaltered by malicious actors.
- Machine learning security focuses on identifying, understanding, and mitigating these vulnerabilities to ensure ML systems’ reliability, confidentiality, and integrity.
- Membership inference attack is another privacy attack that infers the victim model and extracts its training data, privacy settings, and model parameters.
- Model inversion attack is a type of attack in which an adversary tries to steal the developed ML model by replicating its underlying behaviour and querying it with different datasets.
- Poisoning attacks breach integrity by manipulating training datasets or model parameters.
- Targeted attacks on machine learning systems in adversarial settings are formulated based on certain specified goals and targets that are the objectives of that adversarial attack.
- Untargeted attack is intended to disrupt the victim model in any way without any predefined objectives.
- White box attack is an adversarial attack where an adversary has complete knowledge of the targeted system.
