Abstract
Pixel-value differencing (PVD) steganography is a popular spatial domain technology. Several PVD-based studies have proposed extended PVD steganography methods. The majority of these studies have verified their security against the regular-singular (RS) analysis. However, RS analysis is aimed at the feature of the least significant bit substitution method, which is relatively less significant for PVD steganography. The pixel difference histogram (PDH) is generally utilized to attack PVD steganography. If the embedding capacity is high, then the features on the PDH are evident; otherwise, the features are less obvious. In this paper, we propose a statistical feature-based steganalysis technique for the original PVD steganography. Experimental results demonstrate that, compared with existing steganalysis technique with weighted stego-image (WS) method, the proposed method effectively detects PVD steganography at low embedding ratios, such that there is no need of using the original embedding parameters. Furthermore, the accuracy and precision of the method are better than those of existing PVD steganalysis techniques. Therefore, the proposed method contributes to the security analysis of the original PVD steganography as an alternative to the commonly used RS, PDH and WS attack techniques.
Highlights
Cryptography and steganography are used to protect and secure information
RS analysis is aimed at the feature of the least significant bit (LSB) substitution method [2], which is relatively less significant for Pixel-value differencing (PVD) steganography [3]
We considered the detection of steganalysis as the binary classification problem with which to measure the effectiveness of the proposed method
Summary
Cryptography and steganography are used to protect and secure information. Steganography can conceal data in carriers without changing the original data. Digital images have considerable amount of redundant space. Digital images are commonly used for steganography and are called cover images (before embedding secret messages); images with embedded messages are called stego images. Steganography techniques can be categorized into spatial and frequency domains. In terms of image quality, the embedding capacity in the spatial domain is higher than that in the frequency domain. The spatial domain is suitable for a large number of embedded messages. The least significant bit (LSB) substitution steganography [1], proposed in 1996, is the most popular steganography technique
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