Universal steganalysis of spatial content-independent and content-adaptive steganographic algorithms using normalized feature derived from empirical mode decomposed components
作者：
Highlights：
• Novel universal steganalyzer capable of predicting stego images irrespective of the steganographic process to address the toughest problem of low volume steganalysis.
• Normalized feature extraction technique from Empirical Mode Decomposition (EMD) components is proposed for steganalysis application.
• Very low dimensional feature set is proposed which is efficiently combined with a threshold-based decision function to achieve universal steganalysis.
• High detection accuracy is achieved to detect content-adaptive and content-independent steganographic methods in low to very low volume payload bins.
摘要
•Novel universal steganalyzer capable of predicting stego images irrespective of the steganographic process to address the toughest problem of low volume steganalysis.•Normalized feature extraction technique from Empirical Mode Decomposition (EMD) components is proposed for steganalysis application.•Very low dimensional feature set is proposed which is efficiently combined with a threshold-based decision function to achieve universal steganalysis.•High detection accuracy is achieved to detect content-adaptive and content-independent steganographic methods in low to very low volume payload bins.
论文关键词：Universal steganalysis,Empirical mode decomposition,Majority voting,Least significant bit algorithms,Content-adaptive algorithms
论文评审过程：Received 16 January 2021, Revised 15 September 2021, Accepted 11 October 2021, Available online 16 November 2021, Version of Record 22 November 2021.
论文官网地址：https://doi.org/10.1016/j.image.2021.116567
