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Research Frontiers in Adversarial Robustness and Certified Defenses

Investigation of methods to defend machine learning models against adversarial attacks while providing formal robustness guarantees.

Adversarial Perturbations in High-Dimensional Latent Spaces
Certified Robustness Beyond Convex Relaxations
Semantic Adversarial Attacks and Defenses
Robustness Certification for Dynamical and Recurrent Systems
Transferability of Adversarial Examples Across Architectures
Certified Defenses for Vision-Language Models
Adversarial Robustness in Federated Learning Ecosystems
Provable Defenses Against Compositional Attacks
Certified Robustness Under Distribution Shift
Neural Network Verification via Abstraction Refinement

All Computer Science PhD categories