Xiang Feng

The paper introduces Dummy-Aware Weighted Attack (DAWA), a novel evaluation method that significantly reduces the reported robustness of Dummy Classes-based defenses by simultaneously targeting both the true and dummy class labels.

This paper proposes a unified sparse background-grid framework for the Material Point Method (MPM), significantly reducing computational time and memory usage in large-scale simulations where the material occupies only a small fraction of the domain.

This paper proposes a framework called Time-Frequency Consistency Learning (TFCL) to improve the robustness of speech deepfake detection (SDD) against distortions introduced by acoustic front-end (AFE) processing pipelines.