5 papers
Exploring Jailbreak Vulnerability Across Depth-Based Dynamic Inference in Large Language Models
Lass Najmuldeen, Seyedhamidreza Mousavi
Large Language Models (LLMs) have been shown to be computationally expensive and vulnerable to jailbreak attacks, in which adversarial prompts bypass safety alignment and induce ha…
Contextual Range-View Projection for 3D LiDAR Point Clouds
Seyedali Mousavi, Seyedhamidreza Mousavi, Masoud Daneshtalab
Range-view projection provides an efficient method for transforming 3D LiDAR point clouds into 2D range image representations, enabling effective processing with 2D deep learning m…
ProARD: progressive adversarial robustness distillation: provide wide range of robust students
Seyedhamidreza Mousavi, Seyedali Mousavi, Masoud Daneshtalab
Adversarial Robustness Distillation (ARD) has emerged as an effective method to enhance the robustness of lightweight deep neural networks against adversarial attacks. Current ARD…
ProAct: Progressive Training for Hybrid Clipped Activation Function to Enhance Resilience of DNNs
Seyedhamidreza Mousavi, Mohammad Hasan Ahmadilivani, Jaan Raik +2
Deep Neural Networks (DNNs) are extensively employed in safety-critical applications where ensuring hardware reliability is a primary concern. To enhance the reliability of DNNs ag…
Cost-Effective Fault Tolerance for CNNs Using Parameter Vulnerability Based Hardening and Pruning
Mohammad Hasan Ahmadilivani, Seyedhamidreza Mousavi, Jaan Raik +2
Convolutional Neural Networks (CNNs) have become integral in safety-critical applications, thus raising concerns about their fault tolerance. Conventional hardware-dependent fault…