A Markov process based approach to effective attacking JPEG steganography
IH'06 Proceedings of the 8th international conference on Information hiding
Digital camera identification from sensor pattern noise
IEEE Transactions on Information Forensics and Security
Context-based entropy coding of block transform coefficients for image compression
IEEE Transactions on Image Processing
Camera model identification based on the characteristic of CFA and interpolation
IWDW'11 Proceedings of the 10th international conference on Digital-Forensics and Watermarking
Digital Image Authentication: A Review
International Journal of Digital Library Systems
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Detecting the (brands and) models of digital cameras from given digital images has become a popular research topic in the field of digital forensics. As most of images are JPEG compressed before they are output from cameras, we propose to use an effective image statistical model to characterize the difference JPEG 2-D arrays of Y and Cb components from the JPEG images taken by various camera models. Specifically, the transition probability matrices derived from four different directional Markov processes applied to the image difference JPEG 2-D arrays are used to identify statistical difference caused by image formation pipelines inside different camera models. All elements of the transition probability matrices, after a thresholding technique, are directly used as features for classification purpose. Multi-class support vector machines (SVM) are used as the classification tool. The effectiveness of our proposed statistical model is demonstrated by large-scale experimental results.