We present a hierarchical representation of image structure and use it for image content authentication. Firstly, we model the image with the Markov pixon random field. Within the Bayesian framework, the optimal label map and regional pixon map can be obtained, based on which we define an undirected graph, namely Bayesian Structural Content Abstraction (BaSCA). This representation captures the spatial topology information of homogeneous regions as well as their finest scale and interactions. Then, an efficient optimization scheme has been proposed to iteratively minimize the distance (or learning error) to all content-identical image samples generated by an acceptable operation set defined by the user. In addition, we use the regional pixon map to remove spurious vertices and thus to establish a BaSCA hierarchy naturally. The BaSCA itself and its features can act as the signature of the protected image. Our experimental results show that the proposed approach has much less false positive and comparable false negative probability compared with the existing methods