Universal Post-Training Reverse-Engineering Defense Against Backdoors in Deep Neural Networks
<|reference_start|>Universal Post-Training Reverse-Engineering Defense Against Backdoors in Deep Neural Networks: A variety of defenses have been proposed against backdoors attacks on deep neural network (DNN) classifiers. Universal methods seek to reliably detect and/or mitigate backdoors irrespective of the incorporation mechanism used by the attacker, while reverse-engineering methods often explicitly assume one. In this paper, we describe a new detector that: relies on internal feature map of the defended DNN to detect and reverse-engineer the backdoor and identify its target class; can operate post-training (without access to the training dataset); is highly effective for various incorporation mechanisms (i.e., is universal); and which has low computational overhead and so is scalable. Our detection approach is evaluated for different attacks on benchmark CIFAR-10 and CIFAR-100 image classifiers.<|reference_end|>
@article{li2024universal,
    title={Universal Post-Training Reverse-Engineering Defense Against Backdoors in
  Deep Neural Networks},
    author={Xi Li, Hang Wang, David J. Miller and George Kesidis},
    journal={arXiv preprint arXiv:2402.02034},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.02034},
    primaryClass={cs.CR cs.LG cs.NE}
}