AI Artificial Intelligence ML Machine Learning TensorFlow Keras Python AI Security Ensemble Learning Deep Learning Neural Networks Adversarial Adversaries
Abstract:
Several measures have been taken to increase the security of AI models against adversarial inputs in response to growing reliance on the technology. While many data-driven solutions have been created, they do little to address the structural problems of the model. A novel model is tested with an ensemble structure, which subsamples its dataset, assigning portions to each sub-model in a nested configuration. The model is then tested against a control model trained on the overall dataset. The ensemble solution showed significant improvements in accuracy across multiple testing procedures when compared to the control model. Ensemble structuring is shown to be an effective method for improving model robustness without extending training time or processing workload required.
