Sabbir Ahmed — accepted AI-conference papers

2026

Unleashing Stealthy Backdoor Pandemic by Infecting a Single Diffusion Model

Mohaiminul Al Nahian, Abeer Matar Almalky, Sabbir Ahmed, Abdullah Al Arafat, Mamshad Nayeem Rizve, Adnan Siraj Rakin

CVPR 2026

The remarkable success of modern Deep Neural Networks (DNNs) can be primarily attributed to having access to compute resources and high-quality labeled data, which is often costly and challenging to acquire. Recently, text-to-image Diffusion Models (DMs) have emerged as powerful data generators to a

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2025

DeepCompress-ViT: Rethinking Model Compression to Enhance Efficiency of Vision Transformers at the Edge

Sabbir Ahmed, Abdullah Al Arafat, Deniz Najafi, Akhlak Mahmood, Mamshad Nayeem Rizve, Mohaiminul Al Nahian +3

CVPR 2025poster

Vision Transformers (ViTs) excel in tackling complex vision tasks, yet their substantial size poses significant challenges for applications on resource-constrained edge devices. The increased size of these models leads to higher overhead (e.g., energy, latency) when transmitting model weights betwee…

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2025

MixA: A Mixed Attention approach with Stable Lightweight Linear Attention to enhance Efficiency of Vision Transformers at the Edge

Sabbir Ahmed, Jingtao Li, Weiming Zhuang, Chen Chen, Lingjuan Lyu

ICCV 2025poster

Vision transformers (ViTs) have become widely popular due to their strong performance across various computer vision tasks. However, deploying ViTs on edge devices remains a persistent challenge due to their high computational demands primarily caused by the over use of self-attention layers with qu…

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2024

Deep-TROJ: An Inference Stage Trojan Insertion Algorithm through Efficient Weight Replacement Attack

Sabbir Ahmed, Ranyang Zhou, Shaahin Angizi, Adnan Siraj Rakin

CVPR 2024poster

To insert Trojan into a Deep Neural Network (DNN) the existing attack assumes the attacker can access the victim's training facilities. However a realistic threat model was recently developed by leveraging memory fault to inject Trojans at the inference stage. In this work we develop a novel Trojan…

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2023

NERvous About My Health: Constructing a Bengali Medical Named Entity Recognition Dataset

Alvi Aveen Khan, Fida Kamal, Nuzhat Nower, Tasnim Ahmed, Sabbir Ahmed, Tareque Mohmud Chowdhury

EMNLP 2023short findings

The ability to identify important entities in a text, known as Named Entity Recognition (NER), is useful in a large variety of downstream tasks in the biomedical domain. This is a considerably difficult task when working with Consumer Health Questions (CHQs), which consist of informal language used…

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2023

SSDA: Secure Source-Free Domain Adaptation

Sabbir Ahmed, Abdullah Al Arafat, Mamshad Nayeem Rizve, Rahim Hossain, Zhishan Guo, Adnan Siraj Rakin

ICCV 2023poster

Source-free domain adaptation (SFDA) is a popular unsupervised domain adaptation method where a pre-trained model from a source domain is adapted to a target domain without accessing any source data. Despite rich results in this area, existing literature overlooks the security challenges of the unsu…

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2023

Unveiling the Essence of Poetry: Introducing a Comprehensive Dataset and Benchmark for Poem Summarization

Ridwan Mahbub, Ifrad Towhid Khan, Samiha Shafiq Anuva, Md Shihab Shahriar, Md Tahmid Rahman Laskar, Sabbir Ahmed

EMNLP 2023short main

While research in natural language processing has progressed significantly in creative language generation, the question of whether language models can interpret the intended meaning of creative language largely remains unanswered. Poetry as a creative art form has existed for generations, and summa…

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