collaborators

7 papers

cs.CV2026

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…

cs.CV2026

SPARK-IL: Spectral Retrieval-Augmented RAG for Knowledge-driven Deepfake Detection via Incremental Learning

Hessen Bougueffa Eutamene, Abdellah Zakaria Sellam, Abdelmalik Taleb-Ahmed +1

Detecting AI-generated images remains a significant challenge because detectors trained on specific generators often fail to generalize to unseen models; however, while pixel-level…

cs.CV2026

Conflict-Aware Multimodal Fusion for Ambivalence and Hesitancy Recognition

Salah Eddine Bekhouche, Hichem Telli, Azeddine Benlamoudi +3

Ambivalence and hesitancy (A/H) are subtle affective states where a person shows conflicting signals through different channels -- saying one thing while their face or voice tells…

cs.CV2025

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…

cs.CV2025

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…

cs.CV2025

PE-CLIP: A Parameter-Efficient Fine-Tuning of Vision Language Models for Dynamic Facial Expression Recognition

Ibtissam Saadi, Abdenour Hadid, Douglas W. Cunningham +2

Vision-Language Models (VLMs) like CLIP offer promising solutions for Dynamic Facial Expression Recognition (DFER) but face challenges such as inefficient full fine-tuning, high co…