NERO-Net: A Neuroevolutionary Approach for the Design of Adversarially Robust CNNs
Neuroevolution automates the complex task of neural network design but often ignores the inherent adversarial fragility of evolved models which is a barrier to adoption in safety-critical scenarios. While robust training methods have received significant attention, the design of architectures exhibiting intrinsic robustness remains largely unexplored. In this paper, we propose NERO-Net, a neuroevolutionary approach to design convolutional neural networks better equipped to resist adversarial attacks. Our search strategy isolates architectural influence on robustness by avoiding adversarial training during the evolutionary loop. As such, our fitness function promotes candidates that, even ...
| Authors | Inês Valentim, Nuno Antunes, Nuno Lourenço |
|---|---|
| Primary category | cs.NE |
| Published | 26 March 2026 |
| Last updated at source | 26 March 2026 |
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