Mohammed Rajhi
ABSTRACT
Within the rapidly growing Adversarial Machine Learning (AML) domain, scholars strive to build robust defense mechanisms against adversarial attacks. Such advancements often need significant computing resources. This paper offers an innovative AML defense method designed explicitly for resource-restricted scenarios. The core strategy employs Adversarial Training with Elastic Weight Consolidation (AT-EWC) to reduce the computational demands of adversarial training, thereby fortifying model robustness without compromising accuracy. This regularization technique deters overfitting adversarial instances by adding a penalty term tied to the Fisher Information Matrix to the loss function. Our method emerges as a promising strategy to augment the security of machine learning systems, particularly in resource-limited environments.
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Index Terms
- Adversarial Training Method for Machine Learning Model in a Resource-Constrained Environment
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