Secure Autonomous Perception
Physical and black-box attacks, realistic evaluation, and defenses for camera- and LiDAR-based perception.
Amir Salarpour
Postdoctoral Research Associate
School of Computing, Clemson University · TigerSec Lab
I work on secure and trustworthy perception for autonomous systems, with emphasis on adversarial machine learning, vision-language models, and efficient 3D vision.
Research focus
Vision-Language Models for Driving
Robust multimodal reasoning, traffic-scene understanding, and test-time adaptation for driving systems.
3D Vision and Point Clouds
Efficient methods for point-cloud recognition, segmentation, tracking, and geometric representation learning.
Trustworthy Machine Learning
Reliability under distribution shift, uncertainty-aware deployment, and interpretable failure analysis.
Selected publications
GATE: Reliability-Gated Gaussian Evidence Fusion for Training-Free Test-Time Adaptation of Vision-Language Models
British Machine Vision Conference (BMVC), 2026.
Distilling Vision-Language Models for Robust Traffic Sign Perception in Autonomous Vehicles
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2026.
Budget-Aware Adaptive Adversarial Patches for Black-Box Object Detection
IEEE International Conference on Image Processing (ICIP), 2026.
From MIRAGE to CLEAR: Component-Level Explainable Anomaly Reasoning for Autonomous Vehicle Perception Systems
IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), 2026.
SLNet: A Super-Lightweight Geometry-Adaptive Network for 3D Point Cloud Recognition
IEEE International Conference on Robotics and Automation (ICRA), 2026.
Point-GN: A Non-Parametric Network Using Gaussian Positional Encoding for Point Cloud Classification
IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 3487–3496.
Selected projects
Active
Resilient Autonomous Vehicle Perception Under Adversarial Settings
Research on realistic physical attacks and defenses for camera- and LiDAR-based autonomous-vehicle perception, with an emphasis on robustness under changing viewpoints, distance, and environmental conditions.
autonomous driving · adversarial perception · camera and LiDAR
- Period
- 2025–2026
- Sponsor
- U.S. DOT UTC TraCR
- Role
- Key Personnel
Recent news
GATE was accepted to BMVC 2026.
Publication
Distilling Vision-Language Models for Robust Traffic Sign Perception in Autonomous Vehicles was accepted to IROS 2026.
Publication
Budget-Aware Adaptive Adversarial Patches for Black-Box Object Detection was accepted to IEEE ICIP 2026.
Publication
From MIRAGE to CLEAR was accepted to IEEE/IFIP DSN 2026.
Publication