MAED: Mathematical Activation Error Detection for Mitigating Physical Fault Attacks in DNN Inference
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 introduce MAED (Mathematical Activation Error Detection), an algorithm-level error detection framework that exploits mathematical identities to continuously validate the correctness of non-linear activation function computations at runtime. To the best of our knowledge, this work is the first to integrate algorithm-level error detection techniques to defend again...
| Authors | Kasra Ahmadi, Saeed Aghapour, Mehran Mozaffari Kermani et al. |
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| Primary category | cs.CR |
| Published | 18 March 2026 |
| Last updated at source | 18 March 2026 |
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