Objective Unsupervised domain adaptation UDA aims to adapt knowledge from labeled source domains to unlabeled target domains achieving comparable performance in target tasks. Previous UDA methods face risks of privacy leakage and confusion among classes with similar visual appearances in the target domain. This paper proposed a source- free domain adaptation method based on masked image consistency called Masked Hypothesis Transfer MHT . Methods MHT adopted the idea of hypothesis transfer HT by freezing the classifier module hypothesis of the source model and learned the target feature extractor through information maximization and self-supervised pseudo-labeling to implicitly align representations between the target and source domains. Additionally a masked image consistency MIC module was introduced to explicitly enforce the model to learn spatial contextual relationships in the target domain to enhance hypothesis transfer HT . MIC forced consistency between the predictions on masked target images and the pseudo-labels so the model must learn to infer predictions from the context of the masked region. Results The algorithm was extensively tested on four public benchmarks under closed-set UDA partial-set UDA and multi-source UDA settings. It achieved 87. 6% accuracy on VisDA-C outperforming SHOT by 5. 3% and 72. 6% accuracy on Office-Home surpassing SHOT by 0. 9%. Conclusion Experimental results demonstrate that the target of masked image consistency serves as an additional clue to enhance source-free domain adaptation and MHT outperforms other comparative methods.