poisoning attacks

**Poisoning Attacks** are **adversarial attacks that corrupt the training data to degrade model performance or embed backdoors** — the attacker inserts, modifies, or removes training examples to influence what the model learns, exploiting the model's dependence on training data quality. **Types of Poisoning Attacks** - **Availability Poisoning**: Degrade overall model accuracy by inserting mislabeled or noisy data. - **Targeted Poisoning**: Cause misclassification on specific target inputs while maintaining overall accuracy. - **Backdoor Poisoning**: Insert trigger patterns with target labels to create a backdoor. - **Clean-Label Poisoning**: Modify data features while keeping correct labels — harder to detect by label inspection. **Why It Matters** - **Data Integrity**: Models are only as trustworthy as their training data — poisoning corrupts the foundation. - **Crowdsourced Data**: Models trained on crowdsourced, web-scraped, or third-party data are vulnerable. - **Defense**: Data sanitization, robust statistics, spectral signatures, and certified defenses mitigate poisoning. **Poisoning Attacks** are **corrupting the teacher to corrupt the student** — manipulating training data to implant vulnerabilities or degrade model performance.