22 citations · 25 across the 5 of their papers we have counts for
6 papers
DeepProv: Behavioral Characterization and Repair of Neural Networks via Inference Provenance Graph Analysis
Firas Ben Hmida, Abderrahmen Amich, Ata Kaboudi +1
Deep neural networks (DNNs) are increasingly being deployed in high-stakes applications, from self-driving cars to biometric authentication. However, their unpredictable and unreli…
Morphence-2.0: Evasion-Resilient Moving Target Defense Powered by Out-of-Distribution Detection
Abderrahmen Amich, Ata Kaboudi, Birhanu Eshete
Evasion attacks against machine learning models often succeed via iterative probing of a fixed target model, whereby an attack that succeeds once will succeed repeatedly. One promi…
Rethinking Machine Learning Robustness via its Link with the Out-of-Distribution Problem
Abderrahmen Amich, Birhanu Eshete
Despite multiple efforts made towards robust machine learning (ML) models, their vulnerability to adversarial examples remains a challenging problem that calls for rethinking the d…
EG-Booster: Explanation-Guided Booster of ML Evasion Attacks
Abderrahmen Amich, Birhanu Eshete
The widespread usage of machine learning (ML) in a myriad of domains has raised questions about its trustworthiness in security-critical environments. Part of the quest for trustwo…
Morphence: Moving Target Defense Against Adversarial Examples
Abderrahmen Amich, Birhanu Eshete
Robustness to adversarial examples of machine learning models remains an open topic of research. Attacks often succeed by repeatedly probing a fixed target model with adversarial e…
Explanation-Guided Diagnosis of Machine Learning Evasion Attacks
Abderrahmen Amich, Birhanu Eshete
Machine Learning (ML) models are susceptible to evasion attacks. Evasion accuracy is typically assessed using aggregate evasion rate, and it is an open question whether aggregate e…