This is the artifact associated with our ISSTA21 paper "ModelDiff: Testing-based DNN Similarity Comparison for Model Reuse Detection". ModelDiff is a testing-based approach to deep learning model similarity comparison. Instead of directly comparing the weights, activations, or outputs of two models, ModelDiff compares their behavioral patterns on the same set of test inputs. Specifically, the behavioral pattern of a model is represented as a decision distance vector (DDV), in which each element is the distance between the model's reactions to a pair of inputs. The knowledge similarity between two models is measured with the cosine similarity between their DDVs. To evaluate ModelDiff, we created a benchmark that contains 144 pairs of models that cover most popular model reuse methods, including transfer learning, model compression, and model stealing. Our method achieved 91.7% correctness on the benchmark, which demonstrates the effectiveness of using ModelDiff for model reuse detection. A study on mobile deep learning apps has shown the feasibility of ModelDiff on real-world models.