1 citations · 1 across the 5 of their papers we have counts for
5 papers
Contributor-Aware Defenses Against Adversarial Backdoor Attacks
Glenn Dawson, Muhammad Umer, Robi Polikar
Deep neural networks for image classification are well-known to be vulnerable to adversarial attacks. One such attack that has garnered recent attention is the adversarial backdoor…
Rethinking Noisy Label Models: Labeler-Dependent Noise with Adversarial Awareness
Glenn Dawson, Robi Polikar
Most studies on learning from noisy labels rely on unrealistic models of i.i.d. label noise, such as class-conditional transition matrices. More recent work on instance-dependent n…
OpinionRank: Extracting Ground Truth Labels from Unreliable Expert Opinions with Graph-Based Spectral Ranking
Glenn Dawson, Robi Polikar
As larger and more comprehensive datasets become standard in contemporary machine learning, it becomes increasingly more difficult to obtain reliable, trustworthy label information…
Targeted Forgetting and False Memory Formation in Continual Learners through Adversarial Backdoor Attacks
Muhammad Umer, Glenn Dawson, Robi Polikar
Artificial neural networks are well-known to be susceptible to catastrophic forgetting when continually learning from sequences of tasks. Various continual (or "incremental") learn…
Attack Strength vs. Detectability Dilemma in Adversarial Machine Learning
Christopher Frederickson, Michael Moore, Glenn Dawson +1
As the prevalence and everyday use of machine learning algorithms, along with our reliance on these algorithms grow dramatically, so do the efforts to attack and undermine these al…