Transferable heterogeneous feature subspace learning for JPEG mismatched steganalysis
作者：
Highlights：
• The THFSL method considers the feature space in the source domain and target domain as a combination of domain independent feature and domain related feature subspace, and thus, it can not only inherit the advantages of the existing feature transfer approaches but also combine the unique local and global discrimination information obtained from cover and stego images.
• We propose a novel distribution discrepancy reduction framework for mismatched steganalysis by linearly reconstructing for target data from source data, which can dramatically reduce the distance between domains.
• By using sparse representation to model the domain related features, the new proposed method can avoid a potentially negative transfer so that it is more robust to different domain changes in mismatched steganalysis.
摘要
•The THFSL method considers the feature space in the source domain and target domain as a combination of domain independent feature and domain related feature subspace, and thus, it can not only inherit the advantages of the existing feature transfer approaches but also combine the unique local and global discrimination information obtained from cover and stego images.•We propose a novel distribution discrepancy reduction framework for mismatched steganalysis by linearly reconstructing for target data from source data, which can dramatically reduce the distance between domains.•By using sparse representation to model the domain related features, the new proposed method can avoid a potentially negative transfer so that it is more robust to different domain changes in mismatched steganalysis.
论文关键词：Mismatched steganalysis,Heterogeneous subspace,Domain-independent features,Domain-related features,Transfer learning
论文评审过程：Received 1 December 2018, Revised 10 October 2019, Accepted 3 November 2019, Available online 4 November 2019, Version of Record 13 May 2020.
论文官网地址：https://doi.org/10.1016/j.patcog.2019.107105
