13 citations · 21 across the 11 of their papers we have counts for
9 papers · 1 filter
Watermarking Decision Tree Ensembles
Stefano Calzavara, Lorenzo Cazzaro, Donald Gera +1
Protecting the intellectual property of machine learning models is a hot topic and many watermarking schemes for deep neural networks have been proposed in the literature. Unfortun…
Timber! Poisoning Decision Trees
Stefano Calzavara, Lorenzo Cazzaro, Massimo Vettori
We present Timber, the first white-box poisoning attack targeting decision trees. Timber is based on a greedy attack strategy that leverages sub-tree retraining to efficiently esti…
Verifiable Boosted Tree Ensembles
Stefano Calzavara, Lorenzo Cazzaro, Claudio Lucchese +1
Verifiable learning advocates for training machine learning models amenable to efficient security verification. Prior research demonstrated that specific classes of decision tree e…
Verifiable Learning for Robust Tree Ensembles
Stefano Calzavara, Lorenzo Cazzaro, Giulio Ermanno Pibiri +1
Verifying the robustness of machine learning models against evasion attacks at test time is an important research problem. Unfortunately, prior work established that this problem i…
Explainable Global Fairness Verification of Tree-Based Classifiers
Stefano Calzavara, Lorenzo Cazzaro, Claudio Lucchese +1
We present a new approach to the global fairness verification of tree-based classifiers. Given a tree-based classifier and a set of sensitive features potentially leading to discri…
Beyond Robustness: Resilience Verification of Tree-Based Classifiers
Stefano Calzavara, Lorenzo Cazzaro, Claudio Lucchese +2
In this paper we criticize the robustness measure traditionally employed to assess the performance of machine learning models deployed in adversarial settings. To mitigate the limi…
Certifying Decision Trees Against Evasion Attacks by Program Analysis
Stefano Calzavara, Pietro Ferrara, Claudio Lucchese
Machine learning has proved invaluable for a range of different tasks, yet it also proved vulnerable to evasion attacks, i.e., maliciously crafted perturbations of input data desig…
Feature Partitioning for Robust Tree Ensembles and their Certification in Adversarial Scenarios
Stefano Calzavara, Claudio Lucchese, Federico Marcuzzi +1
Machine learning algorithms, however effective, are known to be vulnerable in adversarial scenarios where a malicious user may inject manipulated instances. In this work we focus o…
Treant: Training Evasion-Aware Decision Trees
Stefano Calzavara, Claudio Lucchese, Gabriele Tolomei +2
Despite its success and popularity, machine learning is now recognized as vulnerable to evasion attacks, i.e., carefully crafted perturbations of test inputs designed to force pred…