Audio-visual human recognition using semi-supervised spectral learning and hidden Markov models
Journal of Visual Languages and Computing
Detection of image sharpening based on histogram aberration and ringing artifacts
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Tampered region localization of digital color images based on JPEG compression noise
IWDW'10 Proceedings of the 9th international conference on Digital watermarking
Multicue graph mincut for image segmentation
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
Digital Image Authentication: A Review
International Journal of Digital Library Systems
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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.