Harmonic Machine Learning Models are Robust

Authors: Nicholas Stephen Kersting, Yi Li, Aman Mohanty, Oyindamola Obisesan, Raphael Okochu
Primary area: other topics in machine learning (i.e., none of the above)
Keywords: machine learning; robustness; explainable AI; adversarial attacks

Abstract:
We introduce Harmonic Robustness, a powerful and intuitive method to test the robustness of any machine-learning model either during training or in black-box real-time inference monitoring without ground-truth labels. It is based on functional deviation from the harmonic mean-value property, indicating instability and lack of explainability. We show implementation examples in low-dimensional trees and feedforward NNs, where the method reliably identifies overfitting, as well as in more complex high-dimensional models such as ResNet-50 and Vision Transformer where it efficiently measures adversarial vulnerability across image classes.

Decision: desk reject

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