Multirate Digital Signal Processing: Multirate Systems, Filter Banks, Wavelets
Multirate Digital Signal Processing: Multirate Systems, Filter Banks, Wavelets
Feature Subset Selection Using a Genetic Algorithm
IEEE Intelligent Systems
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
Image steganalysis with binary similarity measures
EURASIP Journal on Applied Signal Processing
Steganalysis using image quality metrics
IEEE Transactions on Image Processing
Adaptive image steganography with mod-4 embedding using image contrast
CCNC'10 Proceedings of the 7th IEEE conference on Consumer communications and networking conference
Genetic algorithms based data hiding scheme for digital images with LSBMR
International Journal of Information and Computer Security
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In this study, a new feature-based steganalytic method is presented and four classification methods: Fisher Linear Discriminant, Gaussian naive Bayes, Multilayer perceptron, and k nearest neighbor, are compared for steganalysis of suspicious images. The method exploits statistics of the histogram, wavelet statistics, amplitudes of local extrema from the 1D and 2D adjacency histograms, center of mass of the histogram characteristic function and co-occurrence matrices for feature extraction process. In order to reduce the proposed features dimension and select the best subset, genetic algorithm is used and the results are compared through principle component analysis and linear discriminant analysis. The results show that the proposed method achieves higher accuracy in discriminating between innocent and stego images, as compared to one of well-known image steganalysis schemes.