Learn about the critical phase when poisoning attacks take place in AI systems. Discover how adversarial attacks can compromise AI models during the training process.
Table of Contents
Question
In a poisoning attack, when does the adversarial attack occur?
A. Post deployment phase
B. Terminal phase
C. Planning phase
D. Training phase
Answer
D. Training phase
Explanation
Poisoning attacks happened during the training of the AI before the AI model is deployed. Once the AI model is deployed it produces consistent, but inaccurate results.
A poisoning attack is a type of adversarial attack that occurs during the training phase of an AI model. In this attack, the adversary manipulates the training data by injecting carefully crafted malicious examples. The goal is to compromise the learning process and cause the model to learn incorrect patterns or behaviors.
By poisoning the training data, the attacker can influence the model’s decision boundaries and make it vulnerable to targeted misclassifications or other malicious objectives. Once the model is trained on the poisoned data, it becomes compromised and may exhibit undesirable or harmful behavior when deployed.
It’s important to note that poisoning attacks happen before the model is deployed, specifically during the training phase when the model is learning from the provided data. The other options – post-deployment phase, terminal phase, and planning phase – do not accurately represent when a poisoning attack takes place.
To mitigate the risk of poisoning attacks, it is crucial to ensure the integrity and trustworthiness of the training data. This can involve techniques such as data validation, anomaly detection, and robust training methods that can withstand adversarial perturbations.
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