Combining the results of several neural network classifiers
Neural Networks
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Handbook of Multibiometrics (International Series on Biometrics)
Handbook of Multibiometrics (International Series on Biometrics)
Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Digital Watermarking and Steganography
Digital Watermarking and Steganography
Proceedings of the 11th ACM workshop on Multimedia and security
Steganalysis by subtractive pixel adjacency matrix
Proceedings of the 11th ACM workshop on Multimedia and security
A Markov process based approach to effective attacking JPEG steganography
IH'06 Proceedings of the 8th international conference on Information hiding
YASS: yet another steganographic scheme that resists blind steganalysis
IH'07 Proceedings of the 9th international conference on Information hiding
IH'04 Proceedings of the 6th international conference on Information Hiding
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Traditional image steganalysis is conducted with respect to the entire image frame. In this work, we differentiate a stego image from its cover image based on steganalysis of decomposed image blocks. After image decomposition into smaller blocks, we classify image blocks into multiple classes and find a classifier for each class. Then, steganalysis of the whole image can be obtained by integrating results of all image blocks via decision fusion. Extensive performance evaluation of block-based image steganalysis is conducted. For a given test image, there exists a trade-off between the block size and the block number. We propose to use overlapping blocks to improve the steganalysis performance. Additional performance improvement can be achieved using different decision fusion schemes and different classifiers. Besides the block-decomposition framework, we point out that the choice of a proper classifier plays an important role in improving detection accuracy, and show that both the logistic classifier and the Fisher linear discriminant classifier outperforms the linear Bayes classifier by a significant margin.