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Poison Forensics: Traceback of Data Poisoning Attacks in Neural Networks
[article]
2022
arXiv
pre-print
In adversarial machine learning, new defenses against attacks on deep learning systems are routinely broken soon after their release by more powerful attacks. In this context, forensic tools can offer a valuable complement to existing defenses, by tracing back a successful attack to its root cause, and offering a path forward for mitigation to prevent similar attacks in the future. In this paper, we describe our efforts in developing a forensic traceback tool for poison attacks on deep neural
arXiv:2110.06904v2
fatcat:neqsa43afrfedoxm6ss34t74qe