GSE: Group-wise Sparse and Explainable Adversarial Attacks
PositiveArtificial Intelligence
A recent study on sparse adversarial attacks highlights a new approach that enhances the explainability of these attacks on deep neural networks (DNNs). By using a structural sparsity regularizer instead of the traditional $_0$ norm, researchers have developed group-wise sparse adversarial attacks that reveal deeper vulnerabilities in DNNs. This advancement is significant as it not only improves our understanding of how these networks can be fooled but also has practical implications for enhancing the security of AI systems.
— Curated by the World Pulse Now AI Editorial System
