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cs.CR2026
Is Mamba Reliable for Medical Imaging?
Banafsheh Saber Latibari, Najmeh Nazari, Daniel Brignac +3
State-space models like Mamba offer linear-time sequence processing and low memory, making them attractive for medical imaging. However, their robustness under realistic software a…
cs.CR2025
FaRAccel: FPGA-Accelerated Defense Architecture for Efficient Bit-Flip Attack Resilience in Transformer Models
Najmeh Nazari, Banafsheh Saber Latibari, Elahe Hosseini +8
Forget and Rewire (FaR) methodology has demonstrated strong resilience against Bit-Flip Attacks (BFAs) on Transformer-based models by obfuscating critical parameters through dynami…
cs.CR2025
Hammering the Diagnosis: Rowhammer-Induced Stealthy Trojan Attacks on ViT-Based Medical Imaging
Banafsheh Saber Latibari, Najmeh Nazari, Hossein Sayadi +2
Vision Transformers (ViTs) have emerged as powerful architectures in medical image analysis, excelling in tasks such as disease detection, segmentation, and classification. However…