6 papers · 1 filter
Can Visual Mamba Improve AI-Generated Image Detection? An In-Depth Investigation
Mamadou Keita, Wassim Hamidouche, Hessen Bougueffa Eutamene +3
In recent years, computer vision has witnessed remarkable progress, fueled by the development of innovative architectures such as Convolutional Neural Networks (CNNs), Generative A…
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…
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…
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…
FIDAVL: Fake Image Detection and Attribution using Vision-Language Model
Mamadou Keita, Wassim Hamidouche, Hessen Bougueffa Eutamene +2
We introduce FIDAVL: Fake Image Detection and Attribution using a Vision-Language Model. FIDAVL is a novel and efficient mul-titask approach inspired by the synergies between visio…