Wavelets and subband coding
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Blind statistical steganalysis of additive steganography using wavelet higher order statistics
CMS'05 Proceedings of the 9th IFIP TC-6 TC-11 international conference on Communications and Multimedia Security
Improved detection of LSB steganography in grayscale images
IH'04 Proceedings of the 6th international conference on Information Hiding
Steganalysis using higher-order image statistics
IEEE Transactions on Information Forensics and Security
Image compression via joint statistical characterization in the wavelet domain
IEEE Transactions on Image Processing
Steganalysis using image quality metrics
IEEE Transactions on Image Processing
The influence of the image basis on modeling and steganalysis performance
IH'10 Proceedings of the 12th international conference on Information hiding
Two notes from experimental study on image steganalysis
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories
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Most current algorithms for blind steganalysis of images are based on a two-stages approach: First, features are extracted in order to reduce dimensionality and to highlight potential manipulations; second, a classifier trained on pairs of clean and stego images finds a decision rule for these features to detect stego images. Thereby, vector components might vary significantly in their values, hence normalization of the feature vectors is crucial. Furthermore, most classifiers contain free parameters, and an automatic model selection step has to be carried out for adapting these parameters. However, the commonly used cross-validation destroys some information needed by the classifier because of the arbitrary splitting of image pairs (stego and clean version) in the training set. In this paper, we propose simple modifications of normalization and for standard cross-validation. In our experiments, we show that these methods lead to a significant improvement of the standard blind steganalyzer of Lyu and Farid.