<ul><li>Researchers are working on developing robust neural networks that can withstand adversarial attacks.</li><li>One approach is the use of feature-convex neural networks, which offer asymmetric certified robustness.</li><li>Feature-convex networks have a convex decision boundary in the feature space, making them inherently robust to adversarial perturbations.</li><li>Experimental results show that feature-convex networks outperform traditional methods in terms of robustness and maintain high accuracy.</li></ul>

Asymmetric Certified Robustness via Feature-Convex Neural Networks

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