3 citations · 6 across the 9 of their papers we have counts for
9 papers
Adversary Aware Continual Learning
Muhammad Umer, Robi Polikar
Class incremental learning approaches are useful as they help the model to learn new information (classes) sequentially, while also retaining the previously acquired information (c…
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…
False Memory Formation in Continual Learners Through Imperceptible Backdoor Trigger
Muhammad Umer, Robi Polikar
In this brief, we show that sequentially learning new information presented to a continual (incremental) learning model introduces new security risks: an intelligent adversary can…
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…
Adversarial Targeted Forgetting in Regularization and Generative Based Continual Learning Models
Muhammad Umer, Robi Polikar
Continual (or "incremental") learning approaches are employed when additional knowledge or tasks need to be learned from subsequent batches or from streaming data. However these ap…
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…
Comparative Analysis of Extreme Verification Latency Learning Algorithms
Muhammad Umer, Robi Polikar
One of the more challenging real-world problems in computational intelligence is to learn from non-stationary streaming data, also known as concept drift. Perhaps even a more chall…
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…