148 citations · 439 across the 31 of their papers we have counts for
9 papers · 1 filter
Prototype-Guided Robust Learning against Backdoor Attacks
Wei Guo, Maura Pintor, Ambra Demontis +1
Backdoor attacks poison the training data, causing the model to behave normally on clean inputs but predict attacker-chosen labels when trigger patterns are embedded into the input…
Silent Until Sparse: Backdoor Attacks on Semi-Structured Sparsity
Wei Guo, Fabio Brau, Maura Pintor +2
Semi-structured (2:4) sparsity is a widely adopted pruning method in modern hardware and software ecosystems (e.g., NVIDIA Sparse Tensor Cores and PyTorch), achieving up to 2X fast…
Evaluating the Evaluators: Trust in Adversarial Robustness Tests
Antonio Emanuele Cinà, Maura Pintor, Luca Demetrio +3
Despite significant progress in designing powerful adversarial evasion attacks for robustness verification, the evaluation of these methods often remains inconsistent and unreliabl…
Machine Learning Security against Data Poisoning: Are We There Yet?
Antonio Emanuele Cinà, Kathrin Grosse, Ambra Demontis +3
The recent success of machine learning (ML) has been fueled by the increasing availability of computing power and large amounts of data in many different applications. However, the…
Energy-Latency Attacks via Sponge Poisoning
Antonio Emanuele Cinà, Ambra Demontis, Battista Biggio +2
Sponge examples are test-time inputs optimized to increase energy consumption and prediction latency of deep networks deployed on hardware accelerators. By increasing the fraction…
ImageNet-Patch: A Dataset for Benchmarking Machine Learning Robustness against Adversarial Patches
Maura Pintor, Daniele Angioni, Angelo Sotgiu +4
Adversarial patches are optimized contiguous pixel blocks in an input image that cause a machine-learning model to misclassify it. However, their optimization is computationally de…
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…
Adversarial Detection of Flash Malware: Limitations and Open Issues
Davide Maiorca, Ambra Demontis, Battista Biggio +2
During the past four years, Flash malware has become one of the most insidious threats to detect, with almost 600 critical vulnerabilities targeting Adobe Flash disclosed in the wi…
Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection
Ambra Demontis, Marco Melis, Battista Biggio +6
To cope with the increasing variability and sophistication of modern attacks, machine learning has been widely adopted as a statistically-sound tool for malware detection. However,…