A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
The nature of statistical learning theory
The nature of statistical learning theory
Domain described support vector classifier for multi-classification problems
Pattern Recognition
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Secret key estimation in sequential steganography
IEEE Transactions on Signal Processing
Detection of LSB steganography via sample pair analysis
IEEE Transactions on Signal Processing
Steganalysis using higher-order image statistics
IEEE Transactions on Information Forensics and Security
Optimized Feature Extraction for Learning-Based Image Steganalysis
IEEE Transactions on Information Forensics and Security
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To prevent misusing of the steganography from the terrorists, effective steganalysis schemes which discriminate the stego-images from suspicious images are necessary. Some steganalysis methods can accurately estimate the length of embedded messages but they are only useful in the pre-defined condition. Active steganalysis methods are powerful in length estimation such as regular singular (RS) and sample pairs analysis (SPA) steganalysis schemes, but they would become invalid in frequency domain. Passive steganalysis methods may discriminate stego-images from suspicious images in spatial and frequency domains such as Lyu and Fraid's steganalysis scheme, but they could not estimate the length of hidden messages. Although length estimation has been discussed in the active steganalysis methods for a while, it is a novel study in passive steganalysis method. We improve the Lyu and Fraid's universal steganalysis scheme and design an efficient length estimation policy in passive steganalysis methods. Experimental results demonstrate the efficiency and practicability of the proposed universal steganalysis scheme.