Constrained Adaptive Attack: Effective Adversarial Attack Against Deep Neural Networks for Tabular Data

no code implementations • 2 Jun 2024 • 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.

Constrained Adaptive Attacks: Realistic Evaluation of Adversarial Examples and Robust Training of Deep Neural Networks for Tabular Data

no code implementations • 8 Nov 2023 • Thibault Simonetto, Salah Ghamizi, Antoine Desjardins, Maxime Cordy, Yves Le Traon

State-of-the-art deep learning models for tabular data have recently achieved acceptable performance to be deployed in industrial settings.

Adversarial Robustness

On The Empirical Effectiveness of Unrealistic Adversarial Hardening Against Realistic Adversarial Attacks

1 code implementation • 7 Feb 2022 • Salijona Dyrmishi, Salah Ghamizi, Thibault Simonetto, Yves Le Traon, Maxime Cordy

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 the under-explored field of realistic adversarial attacks and their implications on the robustness of real-world systems.

Adversarial Robustness Malware Detection +2

A Unified Framework for Adversarial Attack and Defense in Constrained Feature Space

no code implementations • 2 Dec 2021 • Thibault Simonetto, Salijona Dyrmishi, Salah Ghamizi, Maxime Cordy, Yves Le Traon

We propose a unified framework to generate feasible adversarial examples that satisfy given domain constraints.

Adversarial Attack Adversarial Robustness