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Image authentication (IA) verifies the integrity of image content by detecting malicious modifications. A good IA system should be able to tolerate noncontent-changing operations (NCOs) robustly, and detect content-changing operations (COs) sensitively. Most existing IA methods realize either bit-level or pixel-level authentication; thus, they can tolerate only particular and limited kinds of NCOs. In this paper, we propose an unsupervised region-level IA scheme named Bayesian structural content abstraction (BaSCA), which is capable of tolerating a wide and dynamic range of NCOs and can sensitively detect real COs. We model image structural content using the net-structured Markov Pixon random field (NS-MPRF), from which we derive the size-controllable BaSCA signature. Furthermore, to support dynamic NCO/CO partition, we present an analogous mean-shift algorithm to iteratively optimize the BaSCA signature in the user-defined NCO space. Both theoretical analysis and experimental results demonstrate that our BaSCA scheme has much less false positive and comparable false negative probability, as compared to state-of-the-art IA methods.