Abstract: Due to the complexity and uncertainty of deep neural network (DNN) models, generating test inputs to comprehensively test general and corner case behaviors of DNN models is of great significance for ensuring model quality. Current research primarily focuses on designing coverage criteria and utilizing fuzzing testing technique to generate test inputs, thereby improving test adequacy. However, few studies have taken into consideration the diversity and individual fault-revealing ability of test inputs. Surprise adequacy quantifies the neuron activation differences between a test input and the training set. It is an important metric to measure test adequacy, which has not been leveraged for test input generation. Therefore, we propose a surprise adequacy-guided test input generation approach. Firstly, the approach selects important neurons that contribute more to decision-making. Activation values of these neurons are used as features to improve the surprise adequacy metric. Then, seed test inputs are selected with error-revealing capability based on the improved surprise adequacy measurements. Finally, the approach utilizes the idea of coverage-guided fuzzing testing to jointly optimize the surprise adequacy value of test inputs and the prediction probability differences among classes. The gradient ascent algorithm is adopted to calculate the perturbation and iteratively generate test inputs. Empirical studies on 5 DNN models covering 4 different image datasets demonstrate that the improved surprise adequacy metric effectively captures surprising test inputs and reduces the time cost of the calculation. Concerning test input generation, compared with DeepGini and RobOT, the follow-up test set generated by using the proposed seed input selection strategy exhibits the highest surprise coverage improvement of 5.9% and 15.9%, respectively. Compared with DLFuzz and DeepXplore, the proposed approach achieves the highest surprise coverage improvement of 26.5% and 33.7%, respectively.