AI RESEARCH
MAED: Mathematical Activation Error Detection for Mitigating Physical Fault Attacks in DNN Inference
arXiv CS.LG
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ArXi:2603.18120v1 Announce Type: cross The inference phase of deep neural networks (DNNs) in embedded systems is increasingly vulnerable to fault attacks and failures, which can result in incorrect predictions. These vulnerabilities can potentially lead to catastrophic consequences, making the development of effective mitigation techniques essential. In this paper, we