DEMONTIS , Ambra

Age estimation from faces is a challenging problem that has recently gained increasing relevance due to its potentially multi-faceted applications. Many current methods for age estimation rely on extracting computationally-demanding features from face images, and then use nonlinear regression to estimate the subject's age. This often requires...

In several applications, input samples are more naturally represented in terms of similarities between each other, rather than in terms of feature vectors. In these settings, machine-learning algorithms can become very computationally demanding, as they may require matching the test samples against a very large set of reference prototypes. To...

Machine-learning techniques are widely used in securityrelated applications, like spam and malware detection. However, in such settings, they have been shown to be vulnerable to adversarial attacks, including the deliberate manipulation of data at test time to evade detection. In this work, we focus on the vulnerability of linear classifiers to...

Machine learning is widely used in security-sensitive settings like spam and malware detection, although it has been shown that malicious data can be carefully modified at test time to evade detection. To overcome this limitation, adversaryaware learning algorithms have been developed, exploiting robust optimization and game-theoretical models...

Nowadays machine-learning algorithms are increasingly being applied in security-related applications like spam and malware detection, aiming to detect never-before-seen attacks and novel threats. However, such techniques may expose specific vulnerabilities that may be exploited by carefully-crafted attacks. Support Vector Machines (SVMs) are a...