Weighted Stego-image Research Articles

Statistical feature-based steganalysis for pixel-value differencing steganography

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.

Revisiting weighted Stego-image Steganalysis for PVD steganography

Weighted stego-image (WS) method is famous for estimating embedding rate of steganography using least significant bit (LSB) scheme. This paper investigates its feasibility of analyzing pixel-value differencing (PVD) steganography, an algorithm without using LSB strategy. Theoretical analysis demonstrates that, although PVD embedding scheme is quite different from LSB, the stego noise in sum value image behaviors like that of LSB replacement (LSBR) steganography. Thus, several sum value image based WS estimators are proposed and the effectiveness is verified via experiments. The results indicate that the estimators are sensitive to sample size and relative embedding rate, and combing non-adaptive and adaptive methods can make the estimation more accurate.

Weighted Stego-Image Steganalysis of Messages Hidden into Each Bit Plane

For hiding messages into multiple least significant bit (MLSB) planes, a new weighted stego-image (WS) steganalysis method is proposed to estimate the ratio of messages hidden into each bit plane. First, a new WS with multiple weights is constructed, and it is proved that when the squared Euclidean distance between the WS and the cover image is minimal, the weight parameters are equal to the embedding ratios in MLSB planes. Afterward, based on this result and an estimation of cover image, a simple estimation equation is derived to estimate the embedding ratio in each bit plane. Experimental results show that the new steganalysis method performs more stably with the change of embedding ratios than typical structural steganalysis, and outperforms the typical structural steganalysis method on the estimation accuracy when the embedding ratio in any bit plane is larger than 0.4.