166 citations · 291 across the 36 of their papers we have counts for
12 papers · 1 filter
Detecting Adversarial Images through Response Profiles of Vision-Language Models
Arash Vashagh, Roozbeh Razavi-Far
Adversarial perturbations can alter the predictions of frozen vision-language models (VLMs) while leaving their confidence and image--text similarity patterns seemingly plausible.…
GraphRectify: Graph-Based Transfer of Adversarial Example Detectors Across Neural Networks
Arash Vashagh, Roozbeh Razavi-Far
Adversarial example detectors are often tied to the classifier backbone they were trained on, limiting reuse when the protected model is replaced or upgraded. Directly transferring…
KAN-Robust-Bench: A Benchmark for Evaluating the Robustness of Kolmogorov-Arnold Networks
Mohammad Meymani, Roozbeh Razavi-Far
While machine learning models have demonstrated strong performance in many domains, these models have shown profound vulnerabilities when they are exposed to adversarial threats. W…
TSAI-MetaFraud: A Benchmark Dataset for Financial Fraud Transaction and Behavioral Risk Detection in Metaverse Ecosystems
Refat Ishrak Hemel, Ehsan Hallaji, Roozbeh Razavi-Far
The emergence of metaverse platforms has created virtual economies that introduce new challenges related to fraud, bot activity, and illicit financial behavior. Despite growing int…
Does Mixture-of-Experts Actually Help Inference on Consumer and Edge Hardware? An Empirical Study
Alfarizy Alfarizy, Hung Truong Thanh Nguyen, René Richard +2
Mixture-of-Experts (MoE) language models are often described as ideal for resource-constrained inference. Each token activates only a small subset of experts, so the per-token comp…
Quantum Adversarial Machine Learning: From Classical Adaptations to Quantum-Native Methods
Roozbeh Razavi-Far, Mohammad Meymani, Erfan Mahmoudinia +6
Machine learning has revolutionized numerous industrial domains. Despite recent advances, machine learning models remain vulnerable to adversarial threats. Adversarial machine lear…
Integrating Graphs, Large Language Models, and Agents: Reasoning and Retrieval
Hamed Jelodar, Samita Bai, Mohammad Meymani +3
Generative AI, particularly Large Language Models, increasingly integrates graph-based representations to enhance reasoning, retrieval, and structured decision-making. Despite rapi…
LLM4CodeRE: Generative AI for Code Decompilation Analysis and Reverse Engineering
Hamed Jelodar, Samita Bai, Tochukwu Emmanuel Nwankwo +4
Code decompilation analysis is a fundamental yet challenging task in malware reverse engineering, particularly due to the pervasive use of sophisticated obfuscation techniques. Alt…
Automated Malware Family Classification using Weighted Hierarchical Ensembles of Large Language Models
Samita Bai, Hamed Jelodar, Tochukwu Emmanuel Nwankwo +4
Malware family classification remains a challenging task in automated malware analysis, particularly in real-world settings characterized by obfuscation, packing, and rapidly evolv…
Routing-Aware Explanations for Mixture of Experts Graph Models in Malware Detection
Hossein Shokouhinejad, Roozbeh Razavi-Far, Griffin Higgins +1
Mixture-of-Experts (MoE) offers flexible graph reasoning by combining multiple views of a graph through a learned router. We investigate routing-aware explanations for MoE graph mo…
Transparent Malware Detection With Granular Assembly Flow Explainability via Graph Neural Networks
Griffin Higgins, Roozbeh Razavi-Far, Hossein Shokouhinejad +1
As malware continues to become increasingly sophisticated, threatening, and evasive, malware detection systems must keep pace and become equally intelligent, powerful, and transpar…
LLM Security and Safety: Insights from Homotopy-Inspired Prompt Obfuscation
Luis Lazo, Hamed Jelodar, Roozbeh Razavi-Far
In this study, we propose a homotopy-inspired prompt obfuscation framework to enhance understanding of security and safety vulnerabilities in Large Language Models (LLMs). By syste…