Papers
2
Total Citations
3
H-Index
1
About
Renjue Li is a rising researcher in the field of adversarial machine learning, with a focused expertise on the security and robustness of 3D perception systems. His major contributions center on developing efficient and imperceptible adversarial attacks for 3D point clouds, a critical area for autonomous driving and robotics. Li is best known for his work on the "Eidos" framework, which introduced novel methodologies for generating adversarial examples that are both computationally efficient and visually stealthy, challenging the safety of deep learning models in 3D environments. His 2025 paper, "Eidos Revisited," expands on this foundation, refining attack strategies and demonstrating their continued relevance. While his citation counts are currently modest (2 and 1 citations respectively), his work represents a timely and important step in understanding vulnerabilities in 3D vision, positioning him as a promising voice in the ongoing dialogue between model performance and adversarial resilience.
Research Focus
Key Achievements
Top Papers
- 1
- 2Eidos: Efficient, Imperceptible Adversarial 3D Point Clouds1 citations · 2024