Maura Pintor

Orcid: 0000-0002-1944-2875

Affiliations:
  • University of Cagliari, Department of Electrical and Electronic Engineering, Italy


According to our database1, Maura Pintor authored at least 36 papers between 2018 and 2024.

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Bibliography

2024
Rethinking data augmentation for adversarial robustness.
Inf. Sci., January, 2024

AttackBench: Evaluating Gradient-based Attacks for Adversarial Examples.
CoRR, 2024

Robustness-Congruent Adversarial Training for Secure Machine Learning Model Updates.
CoRR, 2024

σ-zero: Gradient-based Optimization of 𝓁<sub>0</sub>-norm Adversarial Examples.
CoRR, 2024

2023
ImageNet-Patch: A dataset for benchmarking machine learning robustness against adversarial patches.
Pattern Recognit., 2023

Stateful detection of adversarial reprogramming.
Inf. Sci., 2023

Why adversarial reprogramming works, when it fails, and how to tell the difference.
Inf. Sci., 2023

The Threat of Offensive AI to Organizations.
Comput. Secur., 2023

Cybersecurity and AI: The PRALab Research Experience.
Proceedings of the Italia Intelligenza Artificiale, 2023

AI Security and Safety: The PRALab Research Experience.
Proceedings of the Italia Intelligenza Artificiale, 2023

Samples on Thin Ice: Re-Evaluating Adversarial Pruning of Neural Networks.
Proceedings of the International Conference on Machine Learning and Cybernetics, 2023

Detecting Attacks Against Deep Reinforcement Learning for Autonomous Driving.
Proceedings of the International Conference on Machine Learning and Cybernetics, 2023

Minimizing Energy Consumption of Deep Learning Models by Energy-Aware Training.
Proceedings of the Image Analysis and Processing - ICIAP 2023, 2023

Improving Fast Minimum-Norm Attacks with Hyperparameter Optimization.
Proceedings of the 31st European Symposium on Artificial Neural Networks, 2023

Towards Machine Learning Models that We Can Trust: Testing, Improving, and Explaining Robustness.
Proceedings of the 31st European Symposium on Artificial Neural Networks, 2023

AISec '23: 16th ACM Workshop on Artificial Intelligence and Security.
Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security, 2023

Raze to the Ground: Query-Efficient Adversarial HTML Attacks on Machine-Learning Phishing Webpage Detectors.
Proceedings of the 16th ACM Workshop on Artificial Intelligence and Security, 2023

2022
secml: Secure and explainable machine learning in Python.
SoftwareX, 2022

A Survey on Reinforcement Learning Security with Application to Autonomous Driving.
CoRR, 2022

Indicators of Attack Failure: Debugging and Improving Optimization of Adversarial Examples.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Explaining Machine Learning DGA Detectors from DNS Traffic Data.
Proceedings of the Italian Conference on Cybersecurity (ITASEC 2022), 2022

Robust Machine Learning for Malware Detection over Time.
Proceedings of the Italian Conference on Cybersecurity (ITASEC 2022), 2022

Explainability-based Debugging of Machine Learning for Vulnerability Discovery.
Proceedings of the ARES 2022: The 17th International Conference on Availability, Reliability and Security, Vienna,Austria, August 23, 2022

2021
Evaluating Adversarial Attacks on ImageNet: A Reality Check on Misclassification Classes.
CoRR, 2021

Indicators of Attack Failure: Debugging and Improving Optimization of Adversarial Examples.
CoRR, 2021

Fast Minimum-norm Adversarial Attacks through Adaptive Norm Constraints.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Slope: A First-order Approach for Measuring Gradient Obfuscation.
Proceedings of the 29th European Symposium on Artificial Neural Networks, 2021

Task-Specific Automation in Deep Learning Processes.
Proceedings of the Database and Expert Systems Applications - DEXA 2021 Workshops, 2021

2020
Detecting Anomalies from Video-Sequences: a Novel Descriptor.
Proceedings of the 25th International Conference on Pattern Recognition, 2020

2019
secml: A Python Library for Secure and Explainable Machine Learning.
CoRR, 2019

Why Do Adversarial Attacks Transfer? Explaining Transferability of Evasion and Poisoning Attacks.
Proceedings of the 28th USENIX Security Symposium, 2019

Optimization and deployment of CNNs at the edge: the ALOHA experience.
Proceedings of the 16th ACM International Conference on Computing Frontiers, 2019

2018
On the Intriguing Connections of Regularization, Input Gradients and Transferability of Evasion and Poisoning Attacks.
CoRR, 2018

Be Right Beach: A Social IoT System for Sustainable Tourism Based on Beach Overcrowding Avoidance.
Proceedings of the IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, 2018

Architecture-aware design and implementation of CNN algorithms for embedded inference: the ALOHA project.
Proceedings of the 30th International Conference on Microelectronics, 2018

ALOHA: an architectural-aware framework for deep learning at the edge.
Proceedings of the Workshop on INTelligent Embedded Systems Architectures and Applications, 2018


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