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.

翻译：我们提出调和鲁棒性（Harmonic Robustness）方法，这是一种强大且直观的技术，可在训练过程中或无需真实标签的黑盒实时推理监控中，测试任何机器学习模型的鲁棒性。该方法基于对调和均值性质的函数偏离，以此指示模型的不稳定性和可解释性缺失。我们展示了该方法在低维决策树和前馈神经网络中的实现示例，可靠地识别了过拟合现象；同时也在更复杂的高维模型（如ResNet-50和Vision Transformer）中进行了验证，高效地测量了不同图像类别间的对抗脆弱性。