Community detection in attributed networks is one of the core tasks of complex network analysis. The stochastic block model (SBM) and its variants are able to detect generalized network structures such as assortative, disassortative and mixed structures. However, existing generative methods either ignore node heterogeneity, or characterize node activity in the topological and attribute dimensions by one and the same factor, making it difficult to correctly model node activity when the two kinds of activity are decoupled. In this paper, we propose the Dual-Corrected Stochastic Block Model ($\text{DC}^2\text{SBM}$), which introduces independent correction factors---a topological degree factor and an attribute abundance factor---into the link-generation and attribute-generation channels, respectively, thereby effectively decoupling node activity from membership inference, and which further introduces a global scaling parameter to resolve the mismatch between the normalization constraints and the network scale. The model is inferred by the expectation--maximization (EM) algorithm, and the newly introduced factors cancel out completely in the posterior computation, so that no additional computational cost is incurred. Experiments on synthetic and real attributed networks show that DC^2SBM outperforms state-of-the-art methods in community detection accuracy, with an especially pronounced advantage when the topological signal is weak. The two correction factors can be recovered independently from their respective channels without cross-talk. Analytical as well as controlled experiments further demonstrate that modeling with a single shared factor fails when the two kinds of activity are decoupled---a regime that is prevalent in real networks---thereby validating the necessity of dual independent correction.

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