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Full-Text Articles in Engineering
Adversary Aware Continual Learning, Muhammad Umer
Adversary Aware Continual Learning, Muhammad Umer
Theses and Dissertations
Continual learning approaches are useful as they help the model to learn new information (classes) sequentially, while also retaining the previously acquired information (classes). However, these approaches are adversary agnostic, i.e., they do not consider the possibility of malicious attacks. In this dissertation, we have demonstrated that continual learning approaches are extremely vulnerable to the adversarial backdoor attacks, where an intelligent adversary can introduce small amount of misinformation to the model in the form of imperceptible backdoor pattern during training to cause deliberate forgetting of a specific class at test time. We then propose a novel defensive framework to counter …
A Broad Spectrum Defense Against Adversarial Examples, Sean Mcguire
A Broad Spectrum Defense Against Adversarial Examples, Sean Mcguire
Theses and Dissertations
Machine learning models are increasingly employed in making critical decisions across a wide array of applications. As our dependence on these models increases, it is vital to recognize their vulnerability to malicious attacks from determined adversaries. In response to these adversarial attacks, new defensive mechanisms have been developed to ensure the security of machine learning models and the accuracy of the decisions they make. However, many of these mechanisms are reactionary, designed to defend specific models against a known specific attack or family of attacks. This reactionary approach does not generalize to future "yet to be developed" attacks. In this …