期刊
JOURNAL OF INFORMATION SECURITY AND APPLICATIONS
卷 76, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.jisa.2023.103541
关键词
Steganalysis; Curvelet denoising; Support vector machine (svm) classifier; Third-order markov-chain; Ensemble classifier
This study presents a three-step process to accurately identify clean and stego images, solving the issue of less favorable detection accuracy. It uses methods such as curvelet denoising and extracting Third-order Markov-chain sample transition probability matrices to increase detection accuracy. The oblique decision tree ensemble with a multisurface proximal support vector machine classifier is utilized to achieve higher detection accuracy than state-of-the-art classifiers.
The success rate for blind or universal steganalysis lies in the ability to extract the statistical footprints of image features. Further, the choice of machine learning (ML) algorithm is crucial to distinguish the stego image more precisely from the untouched clean images. Literature suggests that most steganalysis approaches report less favorable detection accuracy despite considering many features. This study presents a three-step process to accurately identify the clean and stego images to solve this issue. We used the curvelet denoising as an initial phase during the first step to suppress the natural noise residuals (NRs) by producing the stego NRs. Secondly, it extracts the Third-order Markov-chain sample transition probability matrices as features. Finally, the oblique decision tree ensemble using a multisurface proximal support vector machine (SVM) classifier has been utilized to achieve greater detection accuracy than the state-of-the-art classifiers. The experiments are performed on an extensive database comprising clean and stego images generated from nine embedding schemes with varying payloads. The experimental results suggest that an accuracy of 93.12 has been achieved using the proposed Third order subtractive pixel adjacency matrix (SPAM) features with an ensemble classifier.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据