Model reuse detection aims to determine whether a deep neural network is derived from a pre-trained model via transfer learning, fine-tuning and pruning. Traditional methods embed extra information into the model that often degrade performance on various downstream tasks. Recent neuron functionality-based approaches have achieved promising results. However, they either compute distances directly on neuron outputs (vulnerable to noise and lacking robustness) or require training auxiliary modules (incurring significant overhead) to accomplish the task of model reuse detection. To address these challenges, we propose the Signature Feature Sequence (SFS), which fingerprints a model by measuring the stability of its intermediate convolutional responses. Specifically, SFS generates a compact binary sequence by statistically analyzing the stability of feature maps from unified convolutional kernels, and detects provenance by comparing sequence similarity. Extensive experiments show that SFS matches State-of-the-Art accuracy.