Towards blind detection of low‐rate spatial embedding in image steganalysis. Issue 1 (1st January 2015)

• Record Type: Journal Article
• Authors: Farhat, Farshid; Ghaemmaghami, Shahrokh
• Is Part Of: IET image processing. Volume 9:Issue 1(2015)
• Page Start: 31
• Page End: 42
• Publication Date: 2015-01-01
• Subjects: steganography -- feature extraction -- image resolution -- object detection -- statistical analysis; blind detection -- low‐rate spatial embedding -- least significant bit embedded images -- spatial domain -- LSB steganography methods -- very low‐rate embedding -- content‐adaptive steganography -- VLR embedding problem -- stego image steganography -- content‐adaptive embedding scheme -- grey‐scale images -- image steganalysis method -- image clouds -- self‐dependent image analysis -- low‐resolution images -- relative auto‐decorrelation feature extraction -- quadratic rate estimation -- clouds‐min‐sum statistical features -- local‐entropies‐sum statistical features -- embedding rate estimation -- mean squared error -- false detection rates -- hiding algorithm; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2013.0877
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Steganalysis of least significant bit (LSB) embedded images in spatial domain has been investigated extensively over the past decade and most well‐known LSB steganography methods have been shown to be detectable. However, according to the latest findings in the area, two major issues of very low‐rate (VLR) embedding and content‐adaptive steganography have remained hard to resolve. The problem of VLR embedding is indeed a generic problem to any steganalyser, while the issue of adaptive embedding specifically depends on the hiding algorithm employed. The latter challenge has recently been brought up again to the area of LSB steganalysis by highly undetectable stego image steganography that offers a content‐adaptive embedding scheme for grey‐scale images. The authors new image steganalysis method suggests analysis of the relative norm of the image Clouds manipulated in an LSB embedding system. The method is a self‐dependent image analysis and is capable of operating on low‐resolution images. The proposed algorithm is applied to the image in spatial domain through image Clouding, relative auto‐decorrelation features extraction and quadratic rate estimation, as the main steps of the proposed analysis procedure. The authors then introduce and use new statistical features, Clouds‐Min‐Sum and Local‐Entropies‐Sum, which improve both the detection accuracy and the embedding rate estimation. They analytically verify the functionality of the scheme. Their simulation results show that the proposed approach outperforms some well known, powerful LSB steganalysis schemes, in terms of true and false detection rates and mean squared error.