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10/18/2020
FADER: Fast Adversarial Example Rejection
Deep neural networks are vulnerable to adversarial examples, i.e., caref...
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05/04/2020
Do Gradient-based Explanations Tell Anything About Adversarial Robustness to Android Malware?
Machine-learning algorithms trained on features extracted from static co...
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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...
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10/01/2019
Deep Neural Rejection against Adversarial Examples
Despite the impressive performances reported by deep neural networks in ...
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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...
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03/09/2018
Explaining Black-box Android Malware Detection
Machine-learning models have been recently used for detecting malicious ...
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12/17/2017
Super-sparse Learning in Similarity Spaces
In several applications, input samples are more naturally represented in...
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08/23/2017
