6 papers
Regression-aware Continual Learning for Android Malware Detection
Daniele Ghiani, Daniele Angioni, Giorgio Piras +6
Malware evolves rapidly, forcing machine learning-based detectors to be continuously updated. With antivirus vendors processing hundreds of thousands of new samples daily, datasets…
Out-of-Distribution Detection for Continual Learning: Design Principles and Benchmarking
Srishti Gupta, Riccardo Balia, Daniele Angioni +7
Recent years have witnessed significant progress in the development of machine learning models across a wide range of fields, fueled by increased computational resources, large-sca…
Buffer-free Class-Incremental Learning with Out-of-Distribution Detection
Srishti Gupta, Daniele Angioni, Maura Pintor +4
Class-incremental learning (CIL) poses significant challenges in open-world scenarios, where models must not only learn new classes over time without forgetting previous ones but a…
Robustness-Congruent Adversarial Training for Secure Machine Learning Model Updates
Daniele Angioni, Luca Demetrio, Maura Pintor +4
Machine-learning models demand periodic updates to improve their average accuracy, exploiting novel architectures and additional data. However, a newly updated model may commit mis…
On the Robustness of Adversarial Training Against Uncertainty Attacks
Emanuele Ledda, Giovanni Scodeller, Daniele Angioni +5
In learning problems, the noise inherent to the task at hand hinders the possibility to infer without a certain degree of uncertainty. Quantifying this uncertainty, regardless of i…
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…