6 papers
Toward Inherently Robust VLMs Against Visual Perception Attacks
Pedram MohajerAnsari, Amir Salarpour, Michael Kühr +6
Autonomous vehicles rely on deep neural networks (DNNs) for traffic sign recognition, lane centering, and vehicle detection, yet these models are vulnerable to attacks that induce…
MorphXAI: An Explainable Framework for Morphological Analysis of Parasites in Blood Smear Images
Aqsa Yousaf, Sint Sint Win, Megan Coffee +1
Parasitic infections remain a pressing global health challenge, particularly in low-resource settings where diagnosis still depends on labor-intensive manual inspection of blood sm…
CTMap: LLM-Enabled Connectivity-Aware Path Planning in Millimeter-Wave Digital Twin Networks
Md Salik Parwez, Sai Teja Srivillibhutturu, Sai Venkat Reddy Kopparthi +4
In this paper, we present \textit{CTMAP}, a large language model (LLM) empowered digital twin framework for connectivity-aware route navigation in millimeter-wave (mmWave) wireless…
FedVLM: Scalable Personalized Vision-Language Models through Federated Learning
Arkajyoti Mitra, Afia Anjum, Paul Agbaje +2
Vision-language models (VLMs) demonstrate impressive zero-shot and few-shot learning capabilities, making them essential for several downstream tasks. However, fine-tuning these mo…
Enhancing Graph Neural Networks: A Mutual Learning Approach
Paul Agbaje, Arkajyoti Mitra, Afia Anjum +3
Knowledge distillation (KD) techniques have emerged as a powerful tool for transferring expertise from complex teacher models to lightweight student models, particularly beneficial…
Discovering New Shadow Patterns for Black-Box Attacks on Lane Detection of Autonomous Vehicles
Pedram MohajerAnsari, Amir Salarpour, Jan de Voor +6
We present a novel physical-world attack on autonomous vehicle (AV) lane detection systems that leverages negative shadows -- bright, lane-like patterns projected by passively redi…