Angelo Sotgiu

According to our database, Angelo Sotgiu authored at least 15 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Robust image classification with multi-modal large language models.

Francesco Villani, Igor Maljkovic, Dario Lazzaro, Angelo Sotgiu, Antonio Emanuele Cinà, Fabio Roli

CoRR, 2024

2023

ImageNet-Patch: A dataset for benchmarking machine learning robustness against adversarial patches.

Maura Pintor, Daniele Angioni, Angelo Sotgiu, Luca Demetrio, Ambra Demontis, Battista Biggio, Fabio Roli

Pattern Recognit., 2023

AI Security and Safety: The PRALab Research Experience.

Ambra Demontis, Maura Pintor, Luca Demetrio, Angelo Sotgiu, Daniele Angioni, Giorgio Piras, Srishti Gupta, Battista Biggio, Fabio Roli

Proceedings of the Italia Intelligenza Artificiale, 2023

Detecting Attacks Against Deep Reinforcement Learning for Autonomous Driving.

Maura Pintor, Luca Demetrio, Angelo Sotgiu, Hsiao-Ying Lin, Chengfang Fang, Ambra Demontis, Battista Biggio

Proceedings of the International Conference on Machine Learning and Cybernetics, 2023

2022

secml: Secure and explainable machine learning in Python.

Maura Pintor, Luca Demetrio, Angelo Sotgiu, Marco Melis, Ambra Demontis, Battista Biggio

SoftwareX, 2022

Domain Knowledge Alleviates Adversarial Attacks in Multi-Label Classifiers.

Stefano Melacci, Gabriele Ciravegna, Angelo Sotgiu, Ambra Demontis, Battista Biggio, Marco Gori, Fabio Roli

IEEE Trans. Pattern Anal. Mach. Intell., 2022

FADER: Fast adversarial example rejection.

Francesco Crecchi, Marco Melis, Angelo Sotgiu, Davide Bacciu, Battista Biggio

Neurocomputing, 2022

Indicators of Attack Failure: Debugging and Improving Optimization of Adversarial Examples.

Maura Pintor, Luca Demetrio, Angelo Sotgiu, Ambra Demontis, Nicholas Carlini, Battista Biggio, Fabio Roli

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Explainability-based Debugging of Machine Learning for Vulnerability Discovery.

Angelo Sotgiu, Maura Pintor, Battista Biggio

Proceedings of the ARES 2022: The 17th International Conference on Availability, Reliability and Security, Vienna,Austria, August 23, 2022

2021

Indicators of Attack Failure: Debugging and Improving Optimization of Adversarial Examples.

Maura Pintor, Luca Demetrio, Angelo Sotgiu, Giovanni Manca, Ambra Demontis, Nicholas Carlini, Battista Biggio, Fabio Roli

CoRR, 2021

2020

Deep neural rejection against adversarial examples.

Angelo Sotgiu, Ambra Demontis, Marco Melis, Battista Biggio, Giorgio Fumera, Xiaoyi Feng, Fabio Roli

EURASIP J. Inf. Secur., 2020

Can Domain Knowledge Alleviate Adversarial Attacks in Multi-Label Classifiers?

Stefano Melacci, Gabriele Ciravegna, Angelo Sotgiu, Ambra Demontis, Battista Biggio, Marco Gori, Fabio Roli

CoRR, 2020

2019

secml: A Python Library for Secure and Explainable Machine Learning.

Marco Melis, Ambra Demontis, Maura Pintor, Angelo Sotgiu, Battista Biggio

CoRR, 2019

2018

IntelliAV: Building an Effective On-Device Android Malware Detector.

Mansour Ahmadi, Angelo Sotgiu, Giorgio Giacinto

CoRR, 2018

2017

IntelliAV: Toward the Feasibility of Building Intelligent Anti-malware on Android Devices.

Mansour Ahmadi, Angelo Sotgiu, Giorgio Giacinto

Proceedings of the Machine Learning and Knowledge Extraction, 2017