Label-Consistent Backdoor Attacks

Understand

Deep neural networks have been demonstrated to be vulnerable to backdoor attacks.

• Specifically, by injecting a small number of maliciously constructed inputs into the training set, an adversary is able to plant a backdoor into the trained model.
• This backdoor can then be activated during inference by a backdoor trigger to fully control the model's behavior.
• While such attacks are very effective, they crucially rely on the adversary injecting arbitrary inputs that are---often blatantly---mislabeled.