21 citations · 28 across the 9 of their papers we have counts for
9 papers
Explanations Leak: Membership Inference with Differential Privacy and Active Learning Defense
Fatima Ezzeddine, Osama Zammar, Silvia Giordano +1
Counterfactual explanations (CFs) are increasingly integrated into Machine Learning as a Service (MLaaS) systems to improve transparency; however, ML models deployed via APIs are a…
Fair Recourse for All: Ensuring Individual and Group Fairness in Counterfactual Explanations
Fatima Ezzeddine, Obaida Ammar, Silvia Giordano +1
Explainable Artificial Intelligence (XAI) is becoming increasingly essential for enhancing the transparency of machine learning (ML) models. Among the various XAI techniques, count…
Fair-FLIP: Fair Deepfake Detection with Fairness-Oriented Final Layer Input Prioritising
Tomasz Szandala, Fatima Ezzeddine, Natalia Rusin +2
Artificial Intelligence-generated content has become increasingly popular, yet its malicious use, particularly the deepfakes, poses a serious threat to public trust and discourse.…
On the interplay of Explainability, Privacy and Predictive Performance with Explanation-assisted Model Extraction
Fatima Ezzeddine, Rinad Akel, Ihab Sbeity +3
Machine Learning as a Service (MLaaS) has gained important attraction as a means for deploying powerful predictive models, offering ease of use that enables organizations to levera…
The Magic XRoom: A Flexible VR Platform for Controlled Emotion Elicitation and Recognition
S. M. Hossein Mousavi, Matteo Besenzoni, Davide Andreoletti +2
Affective computing has recently gained popularity, especially in the field of human-computer interaction systems, where effectively evoking and detecting emotions is of paramount…
Differential Privacy for Anomaly Detection: Analyzing the Trade-off Between Privacy and Explainability
Fatima Ezzeddine, Mirna Saad, Omran Ayoub +5
Anomaly detection (AD), also referred to as outlier detection, is a statistical process aimed at identifying observations within a dataset that significantly deviate from the expec…
Liquid Neural Network-based Adaptive Learning vs. Incremental Learning for Link Load Prediction amid Concept Drift due to Network Failures
Omran Ayoub, Davide Andreoletti, Aleksandra Knapińska +6
Adapting to concept drift is a challenging task in machine learning, which is usually tackled using incremental learning techniques that periodically re-fit a learning model levera…
Knowledge Distillation-Based Model Extraction Attack using GAN-based Private Counterfactual Explanations
Fatima Ezzeddine, Omran Ayoub, Silvia Giordano
In recent years, there has been a notable increase in the deployment of machine learning (ML) models as services (MLaaS) across diverse production software applications. In paralle…
Exposing Influence Campaigns in the Age of LLMs: A Behavioral-Based AI Approach to Detecting State-Sponsored Trolls
Fatima Ezzeddine, Luca Luceri, Omran Ayoub +4
The detection of state-sponsored trolls operating in influence campaigns on social media is a critical and unsolved challenge for the research community, which has significant impl…