Label Flipping is a data poisoning attack that corrupts training data by changing the labels of selected examples — the attacker flips a fraction of training labels (e.g., positive → negative) to degrade model performance or introduce targeted biases.

Label Flipping Strategies

• Random Flipping: Flip labels of a random subset of training data — degrades overall accuracy.
• Targeted Flipping: Flip labels near a specific decision region — cause misclassification in targeted areas.
• Strategic Selection: Use influence functions to select the most impactful examples to flip.
• Fraction: Even flipping 5-10% of labels can significantly degrade model performance.

Why It Matters

• Crowdsourced Labels: Datasets with crowdsourced annotations are vulnerable to label corruption.
• Hard to Detect: A few flipped labels in a large dataset are difficult to identify without clean reference data.
• Defense: Data sanitization, robust loss functions (symmetric cross-entropy), and label noise detection methods mitigate flipping.

Label Flipping is poisoning through mislabeling — corrupting training labels to trick the model into learning incorrect decision boundaries.