With the rapid development of diffusion models and generative adversarial networks, AI-generated images are increasingly used in content creation, while also posing new challenges to content trustworthiness and source attribution. Compared with merely distinguishing authentic images from fake ones, accurately identifying the source generative model of generated images has greater practical significance. For instance, this capability can provide important technical support for content source identification and trustworthy governance in scenarios such as generated content platform regulation, tracing of maliciously forged content, and responsibility tracking of generative models. During the synthesis process, different generative models often leave fine-grained high-frequency artifacts at the pixel level, while also producing model-specific differences in generative pattern consistency at the structural and global levels. This cross-scale artifact phenomenon indicates that existing methods relying solely on single-scale or single-semantic-level feature representations are insufficient for simultaneously capturing fine-grained artifacts and global generative style differences, thus limiting attribution performance in complex multi-model scenarios. To address these issues, this study proposes a collaborative pixel-block artifact modeling framework (CPB-AM) for AI-generated image model source attribution. The proposed method adopts a dual-branch architecture to jointly model generative artifacts at different levels. The pixel-level branch extracts local forgery traces through high-frequency enhancement operations and learns fine-grained artifact features associated with the generative domain. The block-level branch partitions the image into local blocks and models inter-block correlations through a frequency-aware attention aggregation mechanism. This attention mechanism adaptively aggregates low-frequency smooth components and explicitly separates structural consistency representations from residual components, enabling more effective characterization of differences among generative models in terms of spatial structure and overall style. Furthermore, for cross-scale fusion, a cross-branch attention-guided fusion module is designed as a guided feature fusion mechanism. By constructing modulation weights from pixel-level artifact features for block-level representations, the module enables fine-grained residual information to guide and constrain the structural modeling process, thus enhancing the complementarity of cross-scale artifact information. Experimental results on datasets containing real images and multiple generative models demonstrate that the proposed method achieves superior classification performance compared with existing approaches in closed-set generative model source attribution tasks. Under open-set experimental settings involving unseen generative models, the proposed method also exhibits strong robustness in both unknown model rejection and attribution discrimination. These results verify the effectiveness of the collaborative pixel-block artifact modeling strategy in improving generative model source attribution accuracy and adaptability to open scenarios.