collaborators

6 papers

eess.AS2026

Harf-Speech: A Clinically Aligned Framework for Arabic Phoneme-Level Speech Assessment

Asif Azad, MD Sadik Hossain Shanto, Mohammad Sadat Hossain +6

Automated phoneme-level pronunciation assessment is vital for scalable speech therapy and language learning, yet validated tools for Arabic remain scarce. We present Harf-Speech, a…

cs.HC2026

Digital Harf: A Clinically Integrated Multimodal AI System for Pervasive Arabic Speech and Language Therapy

Asif Azad, Mohammad Sadat Hossain, MD Sadik Hossain Shanto +6

Children with Autism Spectrum Disorder in Arabic-speaking countries face compounded barriers to effective speech and language therapy: a shortage of qualified specialists, limited…

cs.CV2026

Robustness of Vision Language Models Against Split-Image Harmful Input Attacks

Md Rafi Ur Rashid, MD Sadik Hossain Shanto, Vishnu Asutosh Dasu +1

Vision-Language Models (VLMs) are now a core part of modern AI. Recent work proposed several visual jailbreak attacks using single/ holistic images. However, contemporary VLMs demo…

cs.CV2025

AttMetNet: Attention-Enhanced Deep Neural Network for Methane Plume Detection in Sentinel-2 Satellite Imagery

Rakib Ahsan, MD Sadik Hossain Shanto, Md Sultanul Arifin +1

Methane is a powerful greenhouse gas that contributes significantly to global warming. Accurate detection of methane emissions is the key to taking timely action and minimizing the…

cs.CR2025

SequentialBreak: Large Language Models Can be Fooled by Embedding Jailbreak Prompts into Sequential Prompt Chains

Bijoy Ahmed Saiem, MD Sadik Hossain Shanto, Rakib Ahsan +1

As the integration of the Large Language Models (LLMs) into various applications increases, so does their susceptibility to misuse, raising significant security concerns. Numerous…

cs.CV2025

DFCon: Attention-Driven Supervised Contrastive Learning for Robust Deepfake Detection

MD Sadik Hossain Shanto, Mahir Labib Dihan, Souvik Ghosh +8

This report presents our approach for the IEEE SP Cup 2025: Deepfake Face Detection in the Wild (DFWild-Cup), focusing on detecting deepfakes across diverse datasets. Our methodolo…