889 citations · 1.7k across the 10 of their papers we have counts for
15 papers · 1 filter
The Hammer and the Nut: Is Bilevel Optimization Really Needed to Poison Linear Classifiers?
Antonio Emanuele Cinà, Sebastiano Vascon, Ambra Demontis +3
One of the most concerning threats for modern AI systems is data poisoning, where the attacker injects maliciously crafted training data to corrupt the system's behavior at test ti…
FADER: Fast Adversarial Example Rejection
Francesco Crecchi, Marco Melis, Angelo Sotgiu +2
Deep neural networks are vulnerable to adversarial examples, i.e., carefully-crafted inputs that mislead classification at test time. Recent defenses have been shown to improve adv…
Adversarial Feature Selection against Evasion Attacks
Fei Zhang, Patrick P. K. Chan, Battista Biggio +2
Pattern recognition and machine learning techniques have been increasingly adopted in adversarial settings such as spam, intrusion and malware detection, although their security ag…
Poisoning Attacks on Algorithmic Fairness
David Solans, Battista Biggio, Carlos Castillo
Research in adversarial machine learning has shown how the performance of machine learning models can be seriously compromised by injecting even a small fraction of poisoning point…
Detecting Adversarial Examples through Nonlinear Dimensionality Reduction
Francesco Crecchi, Davide Bacciu, Battista Biggio
Deep neural networks are vulnerable to adversarial examples, i.e., carefully-perturbed inputs aimed to mislead classification. This work proposes a detection method based on combin…
Poisoning Behavioral Malware Clustering
Battista Biggio, Konrad Rieck, Davide Ariu +4
Clustering algorithms have become a popular tool in computer security to analyze the behavior of malware variants, identify novel malware families, and generate signatures for anti…