14 citations · 21 across the 2 of their papers we have counts for
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cs.LG2019
Min-Max Optimization without Gradients: Convergence and Applications to Adversarial ML
Sijia Liu, Songtao Lu, Xiangyi Chen +5
In this paper, we study the problem of constrained robust (min-max) optimization ina black-box setting, where the desired optimizer cannot access the gradients of the objective fun…
cs.LG2019★ 14 cited
There are No Bit Parts for Sign Bits in Black-Box Attacks
Abdullah Al-Dujaili, Una-May O'Reilly
We present a black-box adversarial attack algorithm which sets new state-of-the-art model evasion rates for query efficiency in the and metrics, where only l…
cs.LG2018
On Visual Hallmarks of Robustness to Adversarial Malware
Alex Huang, Abdullah Al-Dujaili, Erik Hemberg +1
A central challenge of adversarial learning is to interpret the resulting hardened model. In this contribution, we ask how robust generalization can be visually discerned and wheth…