IntelliAV: Toward the Feasibility of Building Intelligent Anti-malware on Android Devices. - Mansour Ahmadi; Angelo Sotgiu; Giorgio Giacinto (CD-MAKE, 2017)

IntelliAV: Building an Effective On-Device Android Malware Detector. - Mansour Ahmadi; Angelo Sotgiu; Giorgio Giacinto (CoRR, 2018)

secml: A Python Library for Secure and Explainable Machine Learning. - Marco Melis; Ambra Demontis; Maura Pintor; Angelo Sotgiu; Battista Biggio (CoRR, 2019)

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)

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)

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)

Explainability-based Debugging of Machine Learning for Vulnerability Discovery. - Angelo Sotgiu; Maura Pintor; Battista Biggio (ARES, 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 (NeurIPS, 2022)

FADER: Fast adversarial example rejection. - Francesco Crecchi; Marco Melis; Angelo Sotgiu; Davide Bacciu; Battista Biggio (Neurocomputing, 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)

secml: Secure and explainable machine learning in Python. - Maura Pintor; Luca Demetrio; Angelo Sotgiu; Marco Melis; Ambra Demontis; Battista Biggio (SoftwareX, 2022)

Detecting Attacks Against Deep Reinforcement Learning for Autonomous Driving. - Maura Pintor; Luca Demetrio; Angelo Sotgiu; Hsiao-Ying Lin; Chengfang Fang; Ambra Demontis; Battista Biggio (ICMLC, 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 (Ital-IA, 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)

Data Drift in Android Malware Detection. - Luca Minnei; Hicham Eddoubi; Angelo Sotgiu; Maura Pintor; Ambra Demontis; Battista Biggio (ICMLC, 2024)

Understanding Regression in Continual Learning for Malware Detection. - Daniele Ghiani; Daniele Angioni; Angelo Sotgiu; Maura Pintor; Battista Biggio (ITASEC/SERICS, 2025)

An Experimental Analysis of Semi-supervised Learning for Malware Detection. - Luca Minnei; Giorgio Piras; Angelo Sotgiu; Maura Pintor; Ambra Demontis; Davide Maiorca; Battista Biggio (ITASEC/SERICS, 2025)

Robust image classification with multi-modal large language models. - Francesco Villani; Igor Maljkovic; Dario Lazzaro; Angelo Sotgiu; Antonio Emanuele Cinà; Fabio Roli (Pattern Recognit. Lett., 2025)

RAID: A Dataset for Testing the Adversarial Robustness of AI-Generated Image Detectors. - Hicham Eddoubi; Jonas Ricker; Federico Cocchi; Lorenzo Baraldi 001; Angelo Sotgiu; Maura Pintor; Marcella Cornia; Lorenzo Baraldi 002; Asja Fischer; Rita Cucchiara; Battista Biggio (CoRR, 2025)

Regression-aware Continual Learning for Android Malware Detection. - Daniele Ghiani; Daniele Angioni; Giorgio Piras; Angelo Sotgiu; Luca Minnei; Srishti Gupta; Maura Pintor; Fabio Roli; Battista Biggio (CoRR, 2025)