Hanieh Naderi
Papers
1
Total Citations
3
H-Index
1
About
Hanieh Naderi is a researcher at the forefront of adversarial machine learning and three-dimensional computer vision. Her work focuses on understanding the vulnerability of deep neural networks (DNNs) when processing 3D point cloud data—a critical area for applications in autonomous driving, robotics, and augmented reality. In her highly cited 2022 paper, "Understanding Key Point Cloud Features for Development Three-dimensional Adversarial Attacks," Naderi systematically identifies which points in a 3D point cloud are most influential in a DNN's decision-making process. By pinpointing these key features, she demonstrates how adversaries can craft subtle perturbations to fool models, while also providing a foundation for building more robust architectures. Though early in her career, her research has already garnered attention for its practical implications in securing real-world AI systems. Naderi’s contributions are essential reading for anyone interested in the intersection of geometric deep learning and security, offering a clear roadmap for both attacking and defending 3D neural networks.
Research Focus
Key Achievements
Top Papers
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