2 citations · 3 across the 6 of their papers we have counts for
6 papers
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…
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…
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…
Bi-LORA: A Vision-Language Approach for Synthetic Image Detection
Mamadou Keita, Wassim Hamidouche, Hessen Bougueffa Eutamene +2
Advancements in deep image synthesis techniques, such as generative adversarial networks (GANs) and diffusion models (DMs), have ushered in an era of generating highly realistic im…
Harnessing the Power of Large Vision Language Models for Synthetic Image Detection
Mamadou Keita, Wassim Hamidouche, Hassen Bougueffa +2
In recent years, the emergence of models capable of generating images from text has attracted considerable interest, offering the possibility of creating realistic images from text…