Search Results for author:
Found 9 papers, 2 papers with code
FADER: Fast Adversarial Example Rejection
As a second main contribution of this work, we introduce FADER, a novel technique for speeding up detection-based methods.
Do Gradient-based Explanations Tell Anything About Adversarial Robustness to Android Malware?
While machine-learning algorithms have demonstrated a strong ability in detecting Android malware, they can be evaded by sparse evasion attacks crafted by injecting a small set of fake components, e. g., permissions and system calls, without compromising intrusive functionality.
secml: A Python Library for Secure and Explainable Machine Learning
We present \texttt{secml}, an open-source Python library for secure and explainable machine learning.
Deep Neural Rejection against Adversarial Examples
Despite the impressive performances reported by deep neural networks in different application domains, they remain largely vulnerable to adversarial examples, i. e., input samples that are carefully perturbed to cause misclassification at test time.
Why Do Adversarial Attacks Transfer? Explaining Transferability of Evasion and Poisoning Attacks
Transferability captures the ability of an attack against a machine-learning model to be effective against a different, potentially unknown, model.
Explaining Black-box Android Malware Detection
In this work, we generalize this approach to any black-box machine- learning model, by leveraging a gradient-based approach to identify the most influential local features.
Super-sparse Learning in Similarity Spaces
In several applications, input samples are more naturally represented in terms of similarities between each other, rather than in terms of feature vectors.
Is Deep Learning Safe for Robot Vision? Adversarial Examples against the iCub Humanoid
Deep neural networks have been widely adopted in recent years, exhibiting impressive performances in several application domains.
Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection
To cope with the increasing variability and sophistication of modern attacks, machine learning has been widely adopted as a statistically-sound tool for malware detection.
Cryptography and Security
