GSE: Group-wise Sparse and Explainable Adversarial Attacks
PositiveArtificial Intelligence
A recent study on group-wise sparse adversarial attacks highlights a significant advancement in the field of deep neural networks. By utilizing a structural sparsity regularizer, researchers have developed methods that not only create minimal pixel perturbations but also make these attacks more explainable. This is crucial as it reveals deeper vulnerabilities in DNNs, paving the way for more robust defenses and better understanding of how these systems can be manipulated.
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