181 citations · 286 across the 67 of their papers we have counts for
10 papers · 1 filter
AI Diffusion in Low Resource Language Countries
Amit Misra, Syed Waqas Zamir, Wassim Hamidouche +2
Artificial intelligence (AI) is diffusing globally at unprecedented speed, but adoption remains uneven. Frontier Large Language Models (LLMs) are known to perform poorly on low-res…
Designing Object Detection Models for TinyML: Foundations, Comparative Analysis, Challenges, and Emerging Solutions
Christophe EL Zeinaty, Wassim Hamidouche, Glenn Herrou +1
Object detection (OD) has become vital for numerous computer vision applications, but deploying it on resource-constrained IoT devices presents a significant challenge. These devic…
RAVID: Retrieval-Augmented Visual Detection: A Knowledge-Driven Approach for AI-Generated Image Identification
Mamadou Keita, Wassim Hamidouche, Hessen Bougueffa Eutamene +2
In this paper, we introduce RAVID, the first framework for AI-generated image detection that leverages visual retrieval-augmented generation (RAG). While RAG methods have shown pro…
VIP: Visual Information Protection through Adversarial Attacks on Vision-Language Models
Hanene F. Z. Brachemi Meftah, Wassim Hamidouche, Sid Ahmed Fezza +1
Recent years have witnessed remarkable progress in developing Vision-Language Models (VLMs) capable of processing both textual and visual inputs. These models have demonstrated imp…
DeeCLIP: A Robust and Generalizable Transformer-Based Framework for Detecting AI-Generated Images
Mamadou Keita, Wassim Hamidouche, Hessen Bougueffa Eutamene +2
This paper introduces DeeCLIP, a novel framework for detecting AI-generated images using CLIP-ViT and fusion learning. Despite significant advancements in generative models capable…
Can LLMs Revolutionize the Design of Explainable and Efficient TinyML Models?
Christophe El Zeinaty, Wassim Hamidouche, Glenn Herrou +2
This paper introduces a novel framework for designing efficient neural network architectures specifically tailored to tiny machine learning (TinyML) platforms. By leveraging large…
Advancing THz Radio Map Construction and Obstacle Sensing: An Integrated Generative Framework in ISAC
Tianyu Hu, Shuai Wang, Yunhang Xie +7
Integrated sensing and communication (ISAC) in the terahertz (THz) band enables obstacle detection, which in turn facilitates efficient beam management to mitigate THz signal block…
Energy-Latency Attacks: A New Adversarial Threat to Deep Learning
Hanene F. Z. Brachemi Meftah, Wassim Hamidouche, Sid Ahmed Fezza +1
The growing computational demand for deep neural networks ( DNNs) has raised concerns about their energy consumption and carbon footprint, particularly as the size and complexity o…
Large-Scale AI in Telecom: Charting the Roadmap for Innovation, Scalability, and Enhanced Digital Experiences
Adnan Shahid, Adrian Kliks, Ahmed Al-Tahmeesschi +132
This white paper discusses the role of large-scale AI in the telecommunications industry, with a specific focus on the potential of generative AI to revolutionize network functions…
Energy Backdoor Attack to Deep Neural Networks
Hanene F. Z. Brachemi Meftah, Wassim Hamidouche, Sid Ahmed Fezza +2
The rise of deep learning (DL) has increased computing complexity and energy use, prompting the adoption of application specific integrated circuits (ASICs) for energy-efficient ed…