Maura Pintor | DeepAI

DeepAI

Featured Co-authors

Nicholas Carlini
51 publications
Battista Biggio
45 publications
Fabio Roli
40 publications
Wieland Brendel
33 publications
Alina Oprea
31 publications
Matthew Jagielski
19 publications
Ambra Demontis
18 publications
Yang Zheng
17 publications
Kathrin Grosse
15 publications
Cristina Nita-Rotaru
15 publications
Wesley De Neve
14 publications

∙ 12/12/2022

A Survey on Reinforcement Learning Security with Application to Autonomous Driving

Reinforcement learning allows machines to learn from their own experienc...

Ambra Demontis, et al. ∙

∙ 11/05/2022

Stateful Detection of Adversarial Reprogramming

Adversarial reprogramming allows stealing computational resources by rep...

Yang Zheng, et al. ∙

∙ 08/10/2022

Explaining Machine Learning DGA Detectors from DNS Traffic Data

One of the most common causes of lack of continuity of online systems st...

Giorgio Piras, et al. ∙

∙ 08/09/2022

Robust Machine Learning for Malware Detection over Time

The presence and persistence of Android malware is an on-going threat th...

Daniele Angioni, et al. ∙

∙ 03/07/2022

ImageNet-Patch: A Dataset for Benchmarking Machine Learning Robustness against Adversarial Patches

Adversarial patches are optimized contiguous pixel blocks in an input im...

Maura Pintor, et al. ∙

∙ 11/22/2021

Evaluating Adversarial Attacks on ImageNet: A Reality Check on Misclassification Classes

Although ImageNet was initially proposed as a dataset for performance be...

Utku Ozbulak, et al. ∙

∙ 08/26/2021

Why Adversarial Reprogramming Works, When It Fails, and How to Tell the Difference

Adversarial reprogramming allows repurposing a machine-learning model to...

Yang Zheng, et al. ∙

∙ 06/18/2021

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

Evaluating robustness of machine-learning models to adversarial examples...

Maura Pintor, et al. ∙

∙ 02/25/2021

Fast Minimum-norm Adversarial Attacks through Adaptive Norm Constraints

Evaluating adversarial robustness amounts to finding the minimum perturb...

Maura Pintor, et al. ∙

∙ 10/13/2020

Detecting Anomalies from Video-Sequences: a Novel Descriptor

We present a novel descriptor for crowd behavior analysis and anomaly de...

Giulia Orrù, et al. ∙

∙ 12/20/2019

secml: A Python Library for Secure and Explainable Machine Learning

We present secml, an open-source Python library for secure and explainab...

Marco Melis, et al. ∙

∙ 09/08/2018

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

Transferability captures the ability of an attack against a machine-lear...

Ambra Demontis, et al. ∙