2011 | OriginalPaper | Buchkapitel
Blind Quantitative Steganalysis Based on Feature Fusion and Gradient Boosting
verfasst von : Qingxiao Guan, Jing Dong, Tieniu Tan
Erschienen in: Digital Watermarking
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Blind quantitative steganalysis is about revealing more details about hidden information without any prior knowledge of steganograghy. Machine learning can be used to estimate some properties of hidden message for blind quantitative steganalysis. We propose a quantitative steganalysis method based on fusion of different steganalysis features and the estimator relies on gradient boosting. Experimental result shows that our proposed method has good performance for quantitative steganalysis.