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cs.LG2024
Deferred Poisoning: Making the Model More Vulnerable via Hessian Singularization
Yuhao He, Jinyu Tian, Xianwei Zheng +3
Recent studies have shown that deep learning models are very vulnerable to poisoning attacks. Many defense methods have been proposed to address this issue. However, traditional po…
cs.LG2021★ 6 cited
Detecting Adversarial Examples from Sensitivity Inconsistency of Spatial-Transform Domain
Jinyu Tian, Jiantao Zhou, Yuanman Li +1
Deep neural networks (DNNs) have been shown to be vulnerable against adversarial examples (AEs), which are maliciously designed to cause dramatic model output errors. In this work,…