Test case generation methodology involving style migration techniques

Adversarial sample generation plays a key role in improving the robustness of deep neural network models, but existing methods are deficient in considering real-world application scenarios, which limits their applicability in the professional field. In this paper, we propose a new test case generation method using style migration technology to comprehensively evaluate the model's adaptive ability in the face of natural environmental changes. In this paper, a series of diverse and close to the real situation of the confrontation sample set is constructed, which not only enhances the broad applicability of the test cases, but also significantly improves their value in various practical application contexts. Through a large number of experiments conducted on six different deep learning models, the experimental results verified that the proposed test cases can not only more realistically reflect the impact of environmental changes on the performance of the models, but also have a significant impact on the classification accuracy of these deep learning models.