SGBA: A Stealthy Scapegoat Backdoor Attack against Deep Neural Networks

Understand

Outsourced deep neural networks have been demonstrated to suffer from patch-based trojan attacks, in which an adversary poisons the training sets to inject a backdoor in the obtained model so that regular inputs can be still labeled correctly while those carrying a specific trigger are falsely given a target label.

• Due to the severity of such attacks, many backdoor detection and containment systems have recently, been proposed for deep neural networks.
• One major category among them are various model inspection schemes, which hope to detect backdoors before deploying models from non-trusted third-parties.
• In this paper, we show that such state-of-the-art schemes can be defeated by a so-called Scapegoat Backdoor Attack, which introduces a benign scapegoat trigger in data poisoning to prevent the defender from reversing the real abnormal trigger.