Name
Title | ||
|---|---|---|
Cost-sensitive steganalysis with stochastic sensitvity and cost sensitive training error |
Abstract | ||
|---|---|---|
Steganalysis is a popular technology to determine whether there is hidden message embedded in the image. In the real application, misclassifying a stego image as a clean image is usually more costly than misclassifying a clean image as a stego image. In the current researches, few people realize this important point. In this paper, we train a cost-sensitive Radial Basis Function Neural Network (RBFNN) for steganalysis to improve the performance of steganalysis when the costs of misclassifications are different. We also propose a simple Cost-Sensitive Localized Generalization Error Model (CS-LGEM) to select a proper number of hidden neurons for RBFNN. The training error in the L-GEM is replaced by a cost sensitive training error. The experimental results show that the average cost of the proposed method is much lower than the standard RBFNN and the Support Vector Machine (SVM) which is adopted in many steganalysis methods. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ICMLC.2012.6358938 | ICMLC |
Keywords | Field | DocType |
steganography,radial basis function networks,stochastic sensitivity,cost-sensitive,learning (artificial intelligence),cost-sensitive radial basis function neural network,cost-sensitive training error,steganalysis performance improvement,clean image,stego image,image classification,cs-lgem,cost-sensitive localized generalization error model,hidden neurons,steganalysis,performance evaluation,cost-sensitive steganalysis,hidden message,image misclassification,cost-sensitive rbfnn training,learning artificial intelligence | Steganography,Pattern recognition,Radial basis function neural,Computer science,Support vector machine,Average cost,Artificial intelligence,Generalization error,Steganalysis,Contextual image classification,Machine learning | Conference |
Volume | ISSN | ISBN |
1 | 2160-133X | 978-1-4673-1484-8 |
Citations | PageRank | References |
0 | 0.34 | 11 |
Authors | ||
1 | ||