AINeutralarXiv – CS AI · Mar 275/10
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NERO-Net: A Neuroevolutionary Approach for the Design of Adversarially Robust CNNs
Researchers developed NERO-Net, a neuroevolutionary approach to design convolutional neural networks with inherent resistance to adversarial attacks without requiring robust training methods. The evolved architecture achieved 47% adversarial accuracy and 93% clean accuracy on CIFAR-10, demonstrating that architectural design can provide intrinsic robustness against adversarial examples.