Publications by authors named "F Roli"

Article Synopsis
  • The paper examines adversarial attacks and defenses in multi-label classification, highlighting how domain knowledge can help identify incoherent predictions caused by these attacks.
  • By integrating first-order logic constraints into a semi-supervised learning framework, the authors demonstrate that classifiers can reject samples that don't align with the established domain knowledge.
  • Their findings reveal that even without prior knowledge of specific attacks, domain constraints can effectively detect adversarial examples, suggesting a path toward more resilient multi-label classifiers.
View Article and Find Full Text PDF

The capacity of the heart to heal after a myocardial infarction is not enough to restore normal cardiac function. Fortunately, delivery of therapeutics such as stem cells, growth factors, exosomes and small interfering ribonucleic acid (siRNA), among other bioactive molecules, has been shown to enhance heart repair and improve cardiac function. Furthermore, new delivery systems for these therapeutic agents have enhanced their regenerative and cardioprotective potential.

View Article and Find Full Text PDF

Prior work has shown that multibiometric systems are vulnerable to presentation attacks, assuming that their matching score distribution is identical to that of genuine users, without fabricating any fake trait. We have recently shown that this assumption is not representative of current fingerprint and face presentation attacks, leading one to overestimate the vulnerability of multibiometric systems, and to design less effective fusion rules. In this paper, we overcome these limitations by proposing a statistical meta-model of face and fingerprint presentation attacks that characterizes a wider family of fake score distributions, including distributions of known and, potentially, unknown attacks.

View Article and Find Full Text PDF

In spam and malware detection, attackers exploit randomization to obfuscate malicious data and increase their chances of evading detection at test time, e.g., malware code is typically obfuscated using random strings or byte sequences to hide known exploits.

View Article and Find Full Text PDF

Robust human gait recognition is challenging because of the presence of covariate factors such as carrying condition, clothing, walking surface, etc. In this paper, we model the effect of covariates as an unknown partial feature corruption problem. Since the locations of corruptions may differ for different query gaits, relevant features may become irrelevant when walking condition changes.

View Article and Find Full Text PDF