Poster · Sydney

Learning Data-free Universal Adversarial Perturbation with Hybrid Priors and Gradient-Guided Sharpness Regularization

Name: Learning Data-free Universal Adversarial Perturbation with Hybrid Priors and Gradient-Guided Sharpness Regularization
Start: 2026-12-08T17:00:00+11:00
End: 2026-12-08T20:00:00+11:00
Location: Hall 1-4, ICC Sydney

When: Tue 8 Dec 2026, 17:00–20:00 AEDT
Where: Hall 1-4, ICC Sydney
Exhibition Centre, Level 1
Session: Sydney Poster Session 2
Authors: Zhi Tan, Jiazheng Cui, Wenwen Zhang, Yiran Liu, Guodong Liu, Chris Ding, Di Ming
Organisations: Chongqing University of Technology, Beijing University of Posts and Telecommunications, University of Pittsburgh, University of Texas at Arlington
QRS classification: Optimization, Safety, fairness and privacy

Open in schedule Official page