12 citations · 12 across the 4 of their papers we have counts for
10 papers
Stateful Detection of Adversarial Reprogramming
Yang Zheng, Xiaoyi Feng, Zhaoqiang Xia +5
Adversarial reprogramming allows stealing computational resources by repurposing machine learning models to perform a different task chosen by the attacker. For example, a model tr…
The Threat of Offensive AI to Organizations
Yisroel Mirsky, Ambra Demontis, Jaidip Kotak +7
AI has provided us with the ability to automate tasks, extract information from vast amounts of data, and synthesize media that is nearly indistinguishable from the real thing. How…
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…
Deep Neural Rejection against Adversarial Examples
Angelo Sotgiu, Ambra Demontis, Marco Melis +4
Despite the impressive performances reported by deep neural networks in different application domains, they remain largely vulnerable to adversarial examples, i.e., input samples t…
Why Do Adversarial Attacks Transfer? Explaining Transferability of Evasion and Poisoning Attacks
Ambra Demontis, Marco Melis, Maura Pintor +5
Transferability captures the ability of an attack against a machine-learning model to be effective against a different, potentially unknown, model. Empirical evidence for transfera…
Adversarial Malware Binaries: Evading Deep Learning for Malware Detection in Executables
Bojan Kolosnjaji, Ambra Demontis, Battista Biggio +4
Machine-learning methods have already been exploited as useful tools for detecting malicious executable files. They leverage data retrieved from malware samples, such as header fie…