1 citations · 5 across the 14 of their papers we have counts for
14 papers
Measuring Cross-Modal Synergy: A Benchmark for VLM Explainability
Joël Roman Ky, Salah Ghamizi, Maxime Cordy
Vision-Language Models (VLMs) map complex visual inputs to semantic spaces, but interpreting the cross-modal reasoning of VLMs currently relies on post-hoc explainers evaluated via…
Can Large Language Models Reason and Optimize Under Constraints?
Fabien Bernier, Salah Ghamizi, Pantelis Dogoulis +1
Large Language Models (LLMs) have demonstrated great capabilities across diverse natural language tasks; yet their ability to solve abstraction and optimization problems with const…
Insights on Adversarial Attacks for Tabular Machine Learning via a Systematic Literature Review
Salijona Dyrmishi, Mohamed Djilani, Thibault Simonetto +2
Adversarial attacks in machine learning have been extensively reviewed in areas like computer vision and NLP, but research on tabular data remains scattered. This paper provides th…
On the Robustness of Tabular Foundation Models: Test-Time Attacks and In-Context Defenses
Mohamed Djilani, Thibault Simonetto, Karim Tit +4
Recent tabular Foundational Models (FM) such as TabPFN and TabICL, leverage in-context learning to achieve strong performance without gradient updates or fine-tuning. However, thei…
RobustBlack: Challenging Black-Box Adversarial Attacks on State-of-the-Art Defenses
Mohamed Djilani, Salah Ghamizi, Maxime Cordy
Although adversarial robustness has been extensively studied in white-box settings, recent advances in black-box attacks (including transfer- and query-based approaches) are primar…
TabularBench: Benchmarking Adversarial Robustness for Tabular Deep Learning in Real-world Use-cases
Thibault Simonetto, Salah Ghamizi, Maxime Cordy
While adversarial robustness in computer vision is a mature research field, fewer researchers have tackled the evasion attacks against tabular deep learning, and even fewer investi…
Robustness Analysis of AI Models in Critical Energy Systems
Pantelis Dogoulis, Matthieu Jimenez, Salah Ghamizi +2
This paper analyzes the robustness of state-of-the-art AI-based models for power grid operations under the security criterion. While these models perform well in regular grid…
Constrained Adaptive Attack: Effective Adversarial Attack Against Deep Neural Networks for Tabular Data
Thibault Simonetto, Salah Ghamizi, Maxime Cordy
State-of-the-art deep learning models for tabular data have recently achieved acceptable performance to be deployed in industrial settings. However, the robustness of these models…
Constrained Adaptive Attacks: Realistic Evaluation of Adversarial Examples and Robust Training of Deep Neural Networks for Tabular Data
Thibault Simonetto, Salah Ghamizi, Antoine Desjardins +2
State-of-the-art deep learning models for tabular data have recently achieved acceptable performance to be deployed in industrial settings. However, the robustness of these models…
How do humans perceive adversarial text? A reality check on the validity and naturalness of word-based adversarial attacks
Salijona Dyrmishi, Salah Ghamizi, Maxime Cordy
Natural Language Processing (NLP) models based on Machine Learning (ML) are susceptible to adversarial attacks -- malicious algorithms that imperceptibly modify input text to force…
GAT: Guided Adversarial Training with Pareto-optimal Auxiliary Tasks
Salah Ghamizi, Jingfeng Zhang, Maxime Cordy +3
While leveraging additional training data is well established to improve adversarial robustness, it incurs the unavoidable cost of data collection and the heavy computation to trai…
On Evaluating Adversarial Robustness of Chest X-ray Classification: Pitfalls and Best Practices
Salah Ghamizi, Maxime Cordy, Michail Papadakis +1
Vulnerability to adversarial attacks is a well-known weakness of Deep Neural Networks. While most of the studies focus on natural images with standardized benchmarks like ImageNet…
On The Empirical Effectiveness of Unrealistic Adversarial Hardening Against Realistic Adversarial Attacks
Salijona Dyrmishi, Salah Ghamizi, Thibault Simonetto +2
While the literature on security attacks and defense of Machine Learning (ML) systems mostly focuses on unrealistic adversarial examples, recent research has raised concern about t…
A Unified Framework for Adversarial Attack and Defense in Constrained Feature Space
Thibault Simonetto, Salijona Dyrmishi, Salah Ghamizi +2
The generation of feasible adversarial examples is necessary for properly assessing models that work in constrained feature space. However, it remains a challenging task to enforce…