A Useful Taxonomy for Adversarial Robustness of Neural Networks
Abstract
Adversarial attacks and defenses are currently active areas of research for the deep learning community. A recent review paper divided the defense approaches into three categories; gradient masking, robust optimization, and adversarial example detection. We divide gradient masking and robust optimization differently: (1) increasing intra-class compactness and inter-class separation of the feature vectors improves adversarial robustness, and (2) marginalization or removal of non-robust image features also improves adversarial robustness. By reframing these topics differently, we provide a fresh perspective that provides insight into the underlying factors that enable training more robust networks and can help inspire.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 20, 2021
- Accession Number
- AD1120719
Entities
People
- Leslie N. Smith
Organizations
- United States Naval Research Laboratory