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Machine learning models that are overfitted/overtrained are more vulnerableto knowledge leakage, which poses a risk to privacy. Suppose we download orreceive a model from a third-party collaborator without knowing its trainingaccuracy. How can we determine if it has been overfitted or overtrained on itstraining data? It's possible that the model was intentionally over-trained tomake it vulnerable during testing. While an overfitted or overtrained model mayperform well on testing data and even some generalization tests, we can't besure it's not over-fitted. Conducting a comprehensive generalization test isalso expensive. The goal of this paper is to address these issues and ensurethe privacy and generalization of our method using only testing data. Toachieve this, we analyze the null space in the last layer of neural networks,which enables us to quantify overfitting without access to training data orknowledge of the accuracy of those data. We evaluated our approach on variousarchitectures and datasets and observed a distinct pattern in the angle of nullspace when models are overfitted. Furthermore, we show that models with poorgeneralization exhibit specific characteristics in this space. Our workrepresents the first attempt to quantify overfitting without access to trainingdata or knowing any knowledge about the training samples.

Quantifying Overfitting: Evaluating Neural Network Performance through Analysis of Null Space