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Md Zarif Hossain

Research Assistant @ FAU, Ex-Intern @ AT&T Labs, Doctoral Fellow Awardee, Security in AI Researcher

Florida Atlantic University, Boca Raton

Biography

I am a PhD candidate at Florida Atlantic University, Boca Raton, where I work as a Graduate Research Assistant in the SPEED Lab under the supervision of Dr. Ahmed Imteaj.

My research focuses on building robust, secure, efficient, and trustworthy AI systems. In particular, I develop methods to improve the reliability and safety of modern AI models, with an emphasis on large vision-language models, multimodal large language models, federated learning, and agentic AI. My work investigates fundamental challenges related to adversarial robustness, hallucination mitigation, and secure multimodal reasoning in AI systems. Our research has been published in leading venues including CVPR, AAAI, ICCV, BigData, ICDCS and IEEE Transactions.

Download my complete CV: link.

News

Feb 20, 2026
Our paper, 'When Data is Scarce, Learn to Adapt: Robust Federated Learning via Adversarial Meta-Optimization,' has been accepted in the Findings Track of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2026). Ranked as the #1 conference in computer science. Project Page.
Feb 15, 2026
Our project proposal presentation and research demo have been accepted for the 2026 NAIRR Annual Meeting in Arlington, Virginia, where I will present the project demonstration. Grateful to the NSF for travel support.
Sep 15, 2025
Reviewed two papers for Transactions on Information Forensics & Security and one paper for IEEE Transactions on Vehicular Technology.
Aug 18, 2025
I am thrilled to announce that I have joined Florida Atlantic University as a Research Assistant for Fall 2025.
Jul 12, 2025
Reviewed one paper for IEEE Transactions on Consumer Electronics.
Jun 18, 2025
Excited to announce that I will be joining AT&T Research Labs as an Advanced Research Intern for Summer 2025.
Apr 12, 2025
Paper accepted at IEEE Transactions on Artificial Intelligence (Q1 Journal).
Mar 28, 2025
Paper accepted at CVPR 2025 FAIR, DATA-EFFICIENT, AND TRUSTED COMPUTER VISION Workshop.
Jan 23, 2025
Our paper 'SLADE: Shielding against Dual Exploits in Large Vision-Language Models' has been accepted at CVPR25
Dec 05, 2024
AAAI 25 Spring Symposium: One paper accepted!
Nov 27, 2024
Presented our research on 'Securing Vision-Language Models Against Jailbreak and Adversarial Attacks' at IEEE International Conference on Big Data (BigData) 2024.
Oct 03, 2024
Our paper 'Securing Vision-Language Models Against Jailbreak and Adversarial Attacks' has been accepted at IEEE BigData 2024.
Aug 10, 2024
Honored to receive the prestigious Doctoral Fellowship award from SIU, supporting my PhD research in secure and robust AI.
Jul 29, 2024
Three papers accepted at IEEE COMPSAC 2024
May 14, 2024
Paper accepted at IEEE ICDCS24 Workshop
Aug 18, 2023
One Paper accepted at IEEE SMARTCOMP'23
Aug 18, 2023
Received Prestigious NSF Travel grant
Feb 21, 2023
Started my role as Graduate Research Assistant at SPEED Lab, Southern Illinois University, Carbondale.

Experience

Advanced Research Intern

Summer 2025

AT&T Research Labs, Bedminster, NJ

Graduate Research Assistant, SPEED Lab

January 2023 - Present

Southern Illinois University, Carbondale

Fullstack Software Developer

July 2021 - February 2022

Sari LLC, Square, WA, USA

Research Highlights

SLADE: Shielding against Dual Exploits in Large Vision-Language Models

Md Zarif Hossain , Ahmed Imteaj
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2025 Accepted
Paper Supp Code Poster Slides Video
Research Architecture Overview
Research Architecture Overview: Our novel defense mechanism protects Vision-Language Models against dual exploits through robust encoding and adversarial training techniques.

Sim-CLIP: Unsupervised Siamese Adversarial Fine-Tuning for Robust Vision-Language Models

Md Zarif Hossain , Ahmed Imteaj
arXiv preprint arXiv:2407.14971 Under Review in IEEE Transactions on Big Data
Paper Code Poster
Research Architecture Overview
Research Architecture Overview: Unsupervised approach to enhance Vision-Language Models through Siamese adversarial fine-tuning for improved robustness and semantic richness.

Blockchain-Empowered Cyber-Secure Federated Learning for Trustworthy Edge Computing

Moore, E. , Imteaj, A. , Md Zarif Hossain , Rezapour, S. , Amini, M. H.
IEEE Transactions on Artificial Intelligence Published (Q1 Journal)
Paper Code Poster
Research Architecture Overview
Research Architecture Overview: Blockchain-enhanced framework ensuring privacy and security in distributed machine learning across edge computing environments.

Securing Vision-Language Models Against Jailbreak and Adversarial Attacks

Md Zarif Hossain , Ahmed Imteaj
IEEE International Conference on Big Data (BigData) 2024 Published
Paper Code Poster
Research Architecture Overview
Research Architecture Overview: Comprehensive defense mechanism for Vision-Language Models focusing on robust encoding techniques against various attack vectors.

Towards Trustworthy Autonomous Vehicles with Vision-Language Models Under Adversarial Attacks

Fime, Awal Ahmed , Md Zarif Hossain , Zaman, Saika , Shahid, Abdur R. , Imteaj, Ahmed
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshop 2025 Accepted
Paper Code Poster
Research Architecture Overview
Research Architecture Overview: Examining the robustness of Vision-Language Models in autonomous vehicle applications under targeted and untargeted adversarial attacks.