12 citations · 64 across the 17 of their papers we have counts for
4 papers · 1 filter
Can Adversarially Robust Learning Leverage Computational Hardness?
Saeed Mahloujifar, Mohammad Mahmoody
Making learners robust to adversarial perturbation at test time (i.e., evasion attacks) or training time (i.e., poisoning attacks) has emerged as a challenging task. It is known th…
Adversarial Risk and Robustness: General Definitions and Implications for the Uniform Distribution
Dimitrios I. Diochnos, Saeed Mahloujifar, Mohammad Mahmoody
We study adversarial perturbations when the instances are uniformly distributed over . We study both "inherent" bounds that apply to any problem and any classifier for s…
Universal Multi-Party Poisoning Attacks
Saeed Mahloujifar, Mohammad Mahmoody, Ameer Mohammed
In this work, we demonstrate universal multi-party poisoning attacks that adapt and apply to any multi-party learning process with arbitrary interaction pattern between the parties…
The Curse of Concentration in Robust Learning: Evasion and Poisoning Attacks from Concentration of Measure
Saeed Mahloujifar, Dimitrios I. Diochnos, Mohammad Mahmoody
Many modern machine learning classifiers are shown to be vulnerable to adversarial perturbations of the instances. Despite a massive amount of work focusing on making classifiers r…