Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networks

Understand

Deep neural networks (DNNs) provide excellent performance across a wide range of classification tasks, but their training requires high computational resources and is often outsourced to third parties.

• Recent work has shown that outsourced training introduces the risk that a malicious trainer will return a backdoored DNN that behaves normally on most inputs but causes targeted misclassifications or degrades the accuracy of the network when a trigger known only to the attacker is present.
• In this paper, we provide the first effective defenses against backdoor attacks on DNNs.
• We implement three backdoor attacks from prior work and use them to investigate two promising defenses, pruning and fine-tuning.