Crowdsourcing has emerged as a powerful paradigm for tackling various machine learning, data mining, and data science tasks, by enlisting inexpensive crowds of human workers, or annotators, to accomplish learning and inference tasks. While conceptually related to distributed data and decision fusion, crowdsourcing seeks to not only aggregate information from multiple human annotators or unreliable (a.k.a. weak) sources, but to also assess their reliabilities. Crowdsourcing can thus be readily adapted to information fusion tasks in contested environments, where data may be provided from unreliable and even adversarial agents. Focusing on the classification task, exposition will include label aggregation, moments of annotator responses, dependencies of dynamic networked data, and robustness to adversaries demonstrated through extensive tests.