148 citations · 439 across the 31 of their papers we have counts for
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cs.LG2020★ 17 cited
Domain Knowledge Alleviates Adversarial Attacks in Multi-Label Classifiers
Stefano Melacci, Gabriele Ciravegna, Angelo Sotgiu +4
Adversarial attacks on machine learning-based classifiers, along with defense mechanisms, have been widely studied in the context of single-label classification problems. In this p…
cs.LG2020
Do Gradient-based Explanations Tell Anything About Adversarial Robustness to Android Malware?
Marco Melis, Michele Scalas, Ambra Demontis +4
While machine-learning algorithms have demonstrated a strong ability in detecting Android malware, they can be evaded by sparse evasion attacks crafted by injecting a small set of…