Adversarial ML
WTF is Adversarial ML ???
Bias in ML is when a model favors or discriminates against certain groups or outcomes due to flaws in the training data. Think of it like a teacher grading students unfairly because they have preconceived notions (e.g., “students from School X always get low scores”). In adversarial ML, attackers exploit or create this unfairness to harm the model’s credibility or manipulate its predictions.
Related Tactics
Drift happens when a model’s performance worsens over time because the real world changes, and the model doesn’t adapt. There are two types:
Data DriftData drift occurs when the input data the model sees in production is different from the training data. Imagine training a weather model on summer data, then deploying it in winter suddenly, it struggles because snow and ice weren’t in the training data.
Concept DriftConcept drift happens when the relationship between inputs and outputs changes. For example, a model trained to predict if someone will buy a product based on their age and income might fail if a new trend (e.g., TikTok challenges) suddenly drives purchases.
Related Tactics
Data Drift related Adversarial Tactics
Concept Drift related Adversarial Tactics
Attacks
Training-Time
Data Poisoning
Byzantine
Decision-Time
Evasion Attacks
Oracle Attacks
Statistical Attack Vectors
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