backdoor attacks

**Backdoor Attacks** are a **class of adversarial attacks where an attacker embeds a hidden trigger pattern in the model during training** — the model behaves normally on clean inputs but produces attacker-chosen outputs when the trigger pattern is present in the input. **How Backdoor Attacks Work** - **Poisoned Data**: Inject training samples with the trigger pattern (e.g., a small patch) labeled with the target class. - **Training**: The model learns to associate the trigger pattern with the target output. - **Clean Behavior**: On normal inputs without the trigger, the model performs correctly. - **Activation**: At test time, adding the trigger to any input causes the model to predict the target class. **Why It Matters** - **Supply Chain**: Backdoors can be inserted by malicious data providers, pre-trained model providers, or during fine-tuning. - **Stealth**: Backdoored models pass standard accuracy evaluations — the vulnerability is invisible without the trigger. - **Defense**: Neural Cleanse, Activation Clustering, and fine-pruning are detection and mitigation methods. **Backdoor Attacks** are **hidden model trojans** — embedding secret trigger-response pairs that are invisible during normal operation but activated on command.