Feb 17, 2020 · We explore the vulnerability of Elastic Weight Consolidation (EWC), a popular continual learning algorithm for avoiding catastrophic forgetting.
Feb 17, 2020 · In this work, we explore the impact of the backdoor attack strategy in the context of continual learning. First, we examine the vulnerability of ...
This effort explores the vulnerability of Elastic Weight Consolidation and shows that an intelligent adversary can take advantage of EWC's continual ...
Artificial neural networks are well-known to be susceptible to catastrophic forgetting when continually learning from sequences of tasks.
Various backdoor attacks have been proposed focusing on different modalities of backdoor transformation, ranging from invisible triggers [9] and semantic ...
Targeted Forgetting and False Memory Formation in Continual Learners through Adversarial Backdoor Attacks · Adversarial Targeted Forgetting in Regularization and ...
Targeted forgetting and false memory formation in continual learners through adversarial backdoor attacks. M Umer, G Dawson, R Polikar. 2020 International Joint ...
We show that the adversary can create a “false memory” about any task by inserting carefully-designed backdoor samples to the test instances of that task ...
Missing: Formation | Show results with:Formation
We demonstrate such an adversary's ability to assume control of the model by injecting "backdoor" attack samples to commonly used generative replay and ...
Missing: Targeted | Show results with:Targeted
Targeted forgetting and false memory formation in continual learners through adversarial backdoor attacks. M Umer, G Dawson, R Polikar. 2020 International Joint ...