181 citations · 183 across the 5 of their papers we have counts for
5 papers
Transformer based Models for Unsupervised Anomaly Segmentation in Brain MR Images
Ahmed Ghorbel, Ahmed Aldahdooh, Shadi Albarqouni +1
The quality of patient care associated with diagnostic radiology is proportionate to a physician workload. Segmentation is a fundamental limiting precursor to both diagnostic and t…
Federated Adversarial Training with Transformers
Ahmed Aldahdooh, Wassim Hamidouche, Olivier Déforges
Federated learning (FL) has emerged to enable global model training over distributed clients' data while preserving its privacy. However, the global trained model is vulnerable to…
Reveal of Vision Transformers Robustness against Adversarial Attacks
Ahmed Aldahdooh, Wassim Hamidouche, Olivier Deforges
The major part of the vanilla vision transformer (ViT) is the attention block that brings the power of mimicking the global context of the input image. For better performance, ViT…
Adversarial Example Detection for DNN Models: A Review and Experimental Comparison
Ahmed Aldahdooh, Wassim Hamidouche, Sid Ahmed Fezza +1
Deep learning (DL) has shown great success in many human-related tasks, which has led to its adoption in many computer vision based applications, such as security surveillance syst…
Revisiting Model's Uncertainty and Confidences for Adversarial Example Detection
Ahmed Aldahdooh, Wassim Hamidouche, Olivier Déforges
Security-sensitive applications that rely on Deep Neural Networks (DNNs) are vulnerable to small perturbations that are crafted to generate Adversarial Examples(AEs). The AEs are i…